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251. Lyberg, Lars PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Control of the coding operation in statistical investigations: some contributions1981Doktoravhandling, monografi (Annet vitenskapelig)252. Lyberg, Lars PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt1268",{id:"formSmash:items:resultList:1:j_idt1268",widgetVar:"widget_formSmash_items_resultList_1_j_idt1268",onLabel:"Lyberg, Lars ",offLabel:"Lyberg, Lars ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt1271",{id:"formSmash:items:resultList:1:j_idt1271",widgetVar:"widget_formSmash_items_resultList_1_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Kreuter, FraukeUniversity of Maryland.Couper, MickUniversity of Michigan.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The use of paradata to monitor and manage survey data collection2010Inngår i: Proceedings of the American Statistical Association, Section on Survey Research Methods, 2010, Alexandria, VA, USA: American Statistical Association , 2010Konferansepaper (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:1:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_1_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The paper uses a statistical process control perspective to describe how paradata or process data can be used to monitor the survey process. We describe the data and analyses that are available and present several case studies of paradata use in different types of surveys and organizations.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:1:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 253. Lütjohann, Harry PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Linear aggregation in linear regression1974Doktoravhandling, monografi (Annet vitenskapelig)254. Magnúsdóttir, Bergrún Tinna PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt1268",{id:"formSmash:items:resultList:3:j_idt1268",widgetVar:"widget_formSmash_items_resultList_3_j_idt1268",onLabel:"Magnúsdóttir, Bergrún Tinna ",offLabel:"Magnúsdóttir, Bergrún Tinna ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); c-Optimal Designs for the Bivariate Emax Model2013Inngår i: mODa 10 – Advances in Model-Oriented Design and Analysis: Proceedings of the 10th International Workshop in Model-Oriented Design and Analysis Held in Łagów Lubuski, Poland, June 10–14, 2013 / [ed] Dariusz Ucinski, Anthony C. Atkinson, Maciej Patan, Springer, 2013, s. 153-161Konferansepaper (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:3:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_3_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper explores c-optimal design problems for non-linear, multi-response models. A General Equivalence Theorem (GET) is provided and we discuss the influence from model and (co)variance parameters on the designs. Special focus is on a bivariate dose response model, one response being a primary efficacy variable and the other a primary safety variable. The aim is to construct designs that are optimal for estimating the dose that gives the best possible combination of effects and side-effects.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:3:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 255. Magnúsdóttir, Bergrún Tinna PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt1268",{id:"formSmash:items:resultList:4:j_idt1268",widgetVar:"widget_formSmash_items_resultList_4_j_idt1268",onLabel:"Magnúsdóttir, Bergrún Tinna ",offLabel:"Magnúsdóttir, Bergrún Tinna ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation and optimal designs for multi-response Emax models2014Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:4:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_4_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis concerns optimal designs and estimation approaches for a class of nonlinear dose response models, namely multi-response Emax models. These models describe the relationship between the dose of a drug and two or more efficacy and/or safety variables. In order to obtain precise parameter estimates it is important to choose efficient estimation approaches and to use optimal designs to control the level of the doses administered to the patients in the study.

We provide some optimal designs that are efficient for estimating the parameters, a subset of the parameters, and a function of the parameters in multi-response Emax models. The function of interest is an estimate of the best dose to administer to a group of patients. More specifically the dose that maximizes the Clinical Utility Index (CUI) which assesses the net benefit of a drug taking both effects and side-effects into account. The designs derived in this thesis are locally optimal, that is they depend upon the true parameter values. An important part of this thesis is to study how sensitive the optimal designs are to misspecification of prior parameter values.

For multi-response Emax models it is possible to derive maximum likelihood (ML) estimates separately for the parameters in each dose response relation. However, ML estimation can also be carried out simultaneously for all response profiles by making use of dependencies between the profiles (system estimation). In this thesis we compare the performance of these two approaches by using a simulation study where a bivariate Emax model is fitted and by fitting a four dimensional Emax model to real dose response data. The results are that system estimation can substantially increase the precision of parameter estimates, especially when the correlation between response profiles is strong or when the study has not been designed in an efficient way.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:4:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 256. Magnúsdóttir, Bergrún Tinna PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt1268",{id:"formSmash:items:resultList:5:j_idt1268",widgetVar:"widget_formSmash_items_resultList_5_j_idt1268",onLabel:"Magnúsdóttir, Bergrún Tinna ",offLabel:"Magnúsdóttir, Bergrún Tinna ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Optimal design problems for the bivariate Emax modelManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:5:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_5_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Finding a suitable dose is among the most difficult tasks during clinical development of a new drug. In early phases dose finding studies usually focus on finding a safe dose. Safety variables are thus of main interest. In later phases the focus is shifted towards efficacy. Typically a primary efficacy variable is defined and modeled. Various dose-response models have been suggested. For continuous responses among the most successful ones is the Emax model. Here both efficacy and safety are considered simultaneously and the Emax model is extended to a model with a bivariate response, one response being a primary efficacy variable and one being a primary safety variable. This model is referred to as the bivariate Emax model. The focus is on locally c-optimal designs for the bivariate Emax model and a simplified version of it. More specifically the locallyc-optimal designs minimize the asymptotic variance for the estimate of the dose that maximizes the patient's utility. The utility is a function of the efficacy and safety variables and referred to as the Clinical Utility Index (CUI).

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:5:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 257. Magnúsdóttir, Bergrún Tinna PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt1268",{id:"formSmash:items:resultList:6:j_idt1268",widgetVar:"widget_formSmash_items_resultList_6_j_idt1268",onLabel:"Magnúsdóttir, Bergrún Tinna ",offLabel:"Magnúsdóttir, Bergrún Tinna ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Optimal designs for finding the dose that maximizes a Clinical Utility IndexManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:6:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_6_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The importance of dose finding studies in the clinical development process cannot be overstated. Phase I dose finding studies usually focus on finding a safe dose while in later phases the focus is on finding an effective dose. The primary objectives in these different phases are often to estimate the maximum tolerable dose and the minimum effective dose respectively. The ultimate goal of any dose finding study is however to estimate the best dose for each patient. For the obvious reason this is not possible but in this paper we build a framework for designing dose finding studies aimed at estimating the single best dose for a population of patients. A dose that is both safe and effective. We use a utility function to model the patient net benefit from different doses of the drug taking both effects and side-effects into account. We then derive some locally c-optimal designs that minimize the asymptotic variance of the estimated target dose, the dose that maximizes the utility function. We use simulation studies to verify that the designs are appropriate and good even for small sample sizes. Efficacy calculations are carried out to study the impact of misspecification of the model parameters. We concluded that the proposed designs are good when the true parameters are equal to or larger than expected.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:6:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 258. Magnúsdóttir, Bergrún Tinna PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt1268",{id:"formSmash:items:resultList:7:j_idt1268",widgetVar:"widget_formSmash_items_resultList_7_j_idt1268",onLabel:"Magnúsdóttir, Bergrún Tinna ",offLabel:"Magnúsdóttir, Bergrún Tinna ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt1271",{id:"formSmash:items:resultList:7:j_idt1271",widgetVar:"widget_formSmash_items_resultList_7_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nyquist, HansStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Simultaneous estimation of parameters in the bivariate Emax model2015Inngår i: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, nr 28, s. 3714-3723Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:7:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_7_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:7:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 259. Malmberg, Hannes PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt1268",{id:"formSmash:items:resultList:8:j_idt1268",widgetVar:"widget_formSmash_items_resultList_8_j_idt1268",onLabel:"Malmberg, Hannes ",offLabel:"Malmberg, Hannes ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:8:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Argmax over Continuous Indices of Random Variables - An Approach Using Random FieldsManuskript (preprint) (Annet vitenskapelig)260. Malmberg, Hannes PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt1268",{id:"formSmash:items:resultList:9:j_idt1268",widgetVar:"widget_formSmash_items_resultList_9_j_idt1268",onLabel:"Malmberg, Hannes ",offLabel:"Malmberg, Hannes ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:9:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Extremal Behaviour, Weak Convergence and Argmax Theory for a Class of Non-Stationary Marked Point ProcessesManuskript (preprint) (Annet vitenskapelig)261. Malmberg, Hannes PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt1268",{id:"formSmash:items:resultList:10:j_idt1268",widgetVar:"widget_formSmash_items_resultList_10_j_idt1268",onLabel:"Malmberg, Hannes ",offLabel:"Malmberg, Hannes ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:10:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Random Choice over a Continuous Set of Options2013Licentiatavhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:10:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_10_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Random choice theory has traditionally modeled choices over a -nite number of options. This thesis generalizes the literature by studyingthe limiting behavior of choice models as the number of optionsapproach a continuum.The thesis uses the theory of random elds, extreme value theoryand point processes to calculate this limiting behavior. For a numberof distributional assumptions, we can give analytic expressions forthe limiting probability distribution of the characteristics of the bestchoice. In addition, we also outline a straightforward extension to ourtheory which would signicantly relax the distributional assumptionsneeded to derive analytical results.Some examples from commuting research are discussed to illustratepotential applications of the theory.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:10:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 262. Malmberg, Hannes PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt1268",{id:"formSmash:items:resultList:11:j_idt1268",widgetVar:"widget_formSmash_items_resultList_11_j_idt1268",onLabel:"Malmberg, Hannes ",offLabel:"Malmberg, Hannes ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt1271",{id:"formSmash:items:resultList:11:j_idt1271",widgetVar:"widget_formSmash_items_resultList_11_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutet för internationell ekonomi.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hössjer, OlaStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Argmax over continuous indeces of random variables - an approach using random fields2012Rapport (Annet vitenskapelig)263. Malmberg, Hannes PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt1268",{id:"formSmash:items:resultList:12:j_idt1268",widgetVar:"widget_formSmash_items_resultList_12_j_idt1268",onLabel:"Malmberg, Hannes ",offLabel:"Malmberg, Hannes ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt1271",{id:"formSmash:items:resultList:12:j_idt1271",widgetVar:"widget_formSmash_items_resultList_12_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutet för internationell ekonomi.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hössjer, OlaStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Probabilistic choice with an infinite set of options: An Approach Based on Random Sup Measures2014Inngår i: Modern Problems in Insurance Mathematics / [ed] Dmitrii Silvestrov, Anders Martin-Löf, London: Springer, 2014, s. 291-312Kapittel i bok, del av antologi (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:12:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_12_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This chapter deals with probabilistic choice when the number of options is infinite. The choice space is a compact set S⊆R k and we model choice over S as a limit of choices over triangular sequences {x n1 ,…,x nn }⊆S as n→∞ . We employ the theory of random sup measures and show that in the limit when n→∞ , people behave as though they are maximising over a random sup measure. Thus, our results complement Resnick and Roy’s [18] theory of probabilistic choice over infinite sets. They define choice as a maximisation over a stochastic process on S with upper semi-continuous (usc) paths. This connects to our model as their random usc function can be defined as a sup-derivative of a random sup measure, and their maximisation problem can be transformed into a maximisation problem over this random sup measure. One difference remains though: with our model the limiting random sup measures are independently scattered, without usc paths. A benefit of our model is that we provide a way of connecting the stochastic process in their model with finite case distributional assumptions, which are easier to interpret. In particular, when choices are valued additively with one deterministic and one random part, we explore the importance of the tail behaviour of the random part, and show that the exponential distribution is an important boundary case between heavy-tailed and light-tailed distributions.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:12:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 264. Malmros, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt1268",{id:"formSmash:items:resultList:13:j_idt1268",widgetVar:"widget_formSmash_items_resultList_13_j_idt1268",onLabel:"Malmros, Jens ",offLabel:"Malmros, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Some advances in Respondent-driven sampling on directed social networks2013Licentiatavhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:13:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_13_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Respondent-driven sampling (RDS) is one of the most commonly used methods when sampling from hidden or hard-to-reach populations. The RDS methodology combines an improved snowball sampling scheme with a mathematical model that is able to produce unbiased population estimates given that some assumptions about the actual recruitment process are fulfilled. One critical assumption, which is not likely to hold in most cases, is that the underlying social network of the population is undirected. The papers in this thesis provide extensions of RDS theory to populations with partially directed social networks.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:13:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 265. Malmros, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt1268",{id:"formSmash:items:resultList:14:j_idt1268",widgetVar:"widget_formSmash_items_resultList_14_j_idt1268",onLabel:"Malmros, Jens ",offLabel:"Malmros, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:14:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Studies in respondent-driven sampling: Directed networks, epidemics, and random walks2016Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:14:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_14_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Respondent-driven sampling (RDS) is a link-tracing sampling methodology especially suitable for sampling hidden populations. A clever sampling mechanism and inferential procedures that facilitate asymptotically unbiased population estimates has contributed to the rising popularity of the method. The papers in this thesis extend RDS estimation theory to some population structures to which the classical RDS estimation framework is not applicable and analyse the behaviour of the RDS recruitment process.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:14:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 266. Malmros, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt1268",{id:"formSmash:items:resultList:15:j_idt1268",widgetVar:"widget_formSmash_items_resultList_15_j_idt1268",onLabel:"Malmros, Jens ",offLabel:"Malmros, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt1271",{id:"formSmash:items:resultList:15:j_idt1271",widgetVar:"widget_formSmash_items_resultList_15_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Liljeros, FredrikStockholms universitet, Samhällsvetenskapliga fakulteten, Sociologiska institutionen.Britton, TomStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Respondent-driven sampling and an unusual epidemic2016Inngår i: Journal of Applied Probability, ISSN 0021-9002, E-ISSN 1475-6072, Vol. 53, nr 2, s. 518-540Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:15:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_15_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Respondent-driven sampling (RDS) is frequently used when sampling from hidden populations. In RDS, sampled individuals pass on participation coupons to at most c of their acquaintances in the community (c = 3 being a common choice). If these individuals choose to participate, they in turn pass coupons on to their acquaintances, and so on. The process of recruiting is shown to behave like a new Reed-Frost-type network epidemic, in which `becoming infected' corresponds to study participation. We calculate R-0, the probability of a major `outbreak', and the relative size of a major outbreak for c < infinity in the limit of infinite population size and compare to the standard Reed-Frost epidemic. Our results indicate that c should often be chosen larger than in current practice.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:15:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 267. Malmros, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt1268",{id:"formSmash:items:resultList:16:j_idt1268",widgetVar:"widget_formSmash_items_resultList_16_j_idt1268",onLabel:"Malmros, Jens ",offLabel:"Malmros, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt1271",{id:"formSmash:items:resultList:16:j_idt1271",widgetVar:"widget_formSmash_items_resultList_16_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Masuda, NaokiBritton, TomPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Random walks on directed networks: Inference and respondent-driven sampling2016Inngår i: Journal of Official Statistics, ISSN 0282-423X, E-ISSN 2001-7367, Vol. 32, nr 2, s. 433-459Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:16:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_16_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Respondent-driven sampling (RDS) is often used to estimate population properties (e.g., sexual risk behavior) in hard-to-reach populations. In RDS, already sampled individuals recruit population members to the sample from their social contacts in an efficient snowball-like sampling procedure. By assuming a Markov model for the recruitment of individuals, asymptotically unbiased estimates of population characteristics can be obtained. Current RDS estimation methodology assumes that the social network is undirected, that is, all edges are reciprocal. However, empirical social networks in general also include a substantial number of nonreciprocal edges. In this article, we develop an estimation method for RDS in populations connected by social networks that include reciprocal and nonreciprocal edges. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. The proposed estimators are evaluated on artificial and empirical networks and are shown to generally perform better than existing estimators. This is the case in particular when the fraction of directed edges in the network is large.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:16:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 268. Malmros, Jens PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt1268",{id:"formSmash:items:resultList:17:j_idt1268",widgetVar:"widget_formSmash_items_resultList_17_j_idt1268",onLabel:"Malmros, Jens ",offLabel:"Malmros, Jens ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt1271",{id:"formSmash:items:resultList:17:j_idt1271",widgetVar:"widget_formSmash_items_resultList_17_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Rocha, LuisKarolinska Institutet, Stockholm, Sweden; Université de Namur, Belgium.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Multiple seed structure and disconnected networks in respondent-driven samplingManuskript (preprint) (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:17:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_17_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population structure. Several seed individuals are typically used to initiate RDS recruitment. However, standard RDS estimation theory assumes that all sampled individuals originate from only one seed. We use a random walk with teleportation to describe the multiple seed structure of RDS and develop an estimator based on this process. The new estimator is also valid for populations with disconnected social networks. We numerically evaluate our estimator by simulations on artificial and real networks. Our estimator outperforms previous estimators, especially when the proportion of seeds in the sample is large. We recommend our new estimator to be used in RDS studies, in particular when the number of seeds is large or the social network of the population is disconnected.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:17:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 269. Manikas, Vasileios PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt1268",{id:"formSmash:items:resultList:18:j_idt1268",widgetVar:"widget_formSmash_items_resultList_18_j_idt1268",onLabel:"Manikas, Vasileios ",offLabel:"Manikas, Vasileios ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A Bayesian Finite Mixture Model for Network-Telecommunication Data2016Independent thesis Advanced level (degree of Master (Two Years)), 80 poäng / 120 hpOppgaveAbstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:18:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_18_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A data modeling procedure called Mixture model, is introduced beneficial to the characteristics of our data. Mixture models have been proved flexible and easy to use, a situation which can be confirmed from the majority of papers and books which have been published the last twenty years. The models are estimated using a Bayesian inference through an efficient Markov Chain Monte Carlo (MCMC) algorithm, known as Gibbs Sampling. The focus of the paper is on models for network-telecommunication lab data (not time dependent data) and on the valid predictions we can accomplish. We categorize our variables (based on their distribution) in three cases, a mixture of Normal distributions with known allocation, a mixture of Negative Binomial Distributions with known allocations and a mixture of Normal distributions with unknown allocation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:18:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 270. Mathew, Thomas et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt1271",{id:"formSmash:items:resultList:19:j_idt1271",widgetVar:"widget_formSmash_items_resultList_19_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.von Rosen, DietrichSinha, Bimal KumarPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nonnegative estimation of variance components in heteroscedastic one-way random effects ANOVA models2007Inngår i: Research Report, Centre of Biostochastics, Swedish University of Agricultural Sciences, ISSN 1651-8543, nr 1, s. 1-16Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:19:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_19_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); There is considerable amount of literature dealing with inference about the parameters in a heteroscedastic one-way random e®ects ANOVA model. In this paper we primarily address the problem of improved quadratic estimation of the random e®ect variance component. It turns out that such estimators with a smaller MSE compared to some standard unbiased quadratic estimators exist under quite general conditions. Improved estimators of the error variance components are also established.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:19:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 271. Mathew, Thomas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt1268",{id:"formSmash:items:resultList:20:j_idt1268",widgetVar:"widget_formSmash_items_resultList_20_j_idt1268",onLabel:"Mathew, Thomas ",offLabel:"Mathew, Thomas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt1271",{id:"formSmash:items:resultList:20:j_idt1271",widgetVar:"widget_formSmash_items_resultList_20_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Department of Mathematics and Statistics, University of Maryland, Baltimore County, USA.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.von Rosen, DietrichInstitutionen för energi och teknik, SLU, Uppsala.Sinha, Bimal KumarDepartment of Mathematics and Statistics, University of Maryland, Baltimore County, USA.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nonnegative estimation of variance components in heteroscedastic one-way random effects ANOVA models2007Rapport (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:20:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_20_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); There is a considerable amount of literature dealing with inference about the parameters in a heteroscedastic one-way random-effects ANOVA model. In this paper, we primarily address the problem of improved quadratic estimation of the random-effect variance component. It turns out that such estimators with a smaller mean squared error compared with some standard unbiased quadratic estimators exist under quite general conditions. Improved estimators of the error variance components are also established.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:20:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 272. Miller, Frank PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt1268",{id:"formSmash:items:resultList:21:j_idt1268",widgetVar:"widget_formSmash_items_resultList_21_j_idt1268",onLabel:"Miller, Frank ",offLabel:"Miller, Frank ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); When is an adaptive design useful in clinical dose-finding trials?2015Inngår i: Festschrift in Honor of Hans Nyquist on the occasion of his 65th birthday / [ed] Ellinor Fackle-Fornius, Stockholm: Stockholm University, 2015, s. 28-43Kapittel i bok, del av antologi (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:21:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_21_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); During the development process for new drugs, dose-finding trials have to be conducted and the choice of their design is an important issue. Traditionally, the standard design is a balanced design where equally large groups of patients are treated with different doses of the new drug or with a control. However, it has been identified that other innovative designs might be more efficient: Optimal designs which use non-balanced allocation to dose, and adaptive designs where the allocation to the doses can be changed during the study based on results collected earlier in the study. In a simulation study we will compare efficiencies of balanced non-adaptive, optimal non-adaptive, adaptive two-stage and fully sequential adaptive designs. In all situations considered one can gain from applying optimal design theory. However, when moving from the optimal non-adaptive design to an adaptive design, there are situations where the design is improved and other situations where there is only a minor or no gain. Based on our considered situations, we generalize our observations to answer when an adaptive design is useful.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:21:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 273. Miller, Frank PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt1268",{id:"formSmash:items:resultList:22:j_idt1268",widgetVar:"widget_formSmash_items_resultList_22_j_idt1268",onLabel:"Miller, Frank ",offLabel:"Miller, Frank ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt1271",{id:"formSmash:items:resultList:22:j_idt1271",widgetVar:"widget_formSmash_items_resultList_22_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Burman, Carl-FredrikPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A decision theoretical modeling for Phase III investments and drug licensing2018Inngår i: Journal of Biopharmaceutical Statistics, ISSN 1054-3406, E-ISSN 1520-5711, Vol. 28, nr 4, s. 698-721Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:22:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_22_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); For a new candidate drug to become an approved medicine, several decision points have to be passed. In this article, we focus on two of them: First, based on Phase II data, the commercial sponsor decides to invest (or not) in Phase III. Second, based on the outcome of Phase III, the regulator determines whether the drug should be granted market access. Assuming a population of candidate drugs with a distribution of true efficacy, we optimize the two stakeholders' decisions and study the interdependence between them. The regulator is assumed to seek to optimize the total public health benefit resulting from the efficacy of the drug and a safety penalty. In optimizing the regulatory rules, in terms of minimal required sample size and the Type I error in Phase III, we have to consider how these rules will modify the commercial optimization made by the sponsor. The results indicate that different Type I errors should be used depending on the rarity of the disease.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:22:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 274. Miller, Frank PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt1268",{id:"formSmash:items:resultList:23:j_idt1268",widgetVar:"widget_formSmash_items_resultList_23_j_idt1268",onLabel:"Miller, Frank ",offLabel:"Miller, Frank ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt1271",{id:"formSmash:items:resultList:23:j_idt1271",widgetVar:"widget_formSmash_items_resultList_23_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Fackle-Fornius, EllinorStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.Nyquist, HansStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Maximin Efficient Designs for Estimating the Interesting Part of a Dose-Effect Curve2013Inngår i: 6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013): , 2013Konferansepaper (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:23:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_23_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); As the costs of clinical studies increase, the demand for more efficient designs also increases. Therefore, there is a growing interest in introducing designs that optimize precision in clinical studies. Unfortunately, optimal designs generally require knowledge of unknown parameters. We consider the maximin approach to handle this problem. A maximin efficient design maximizes the efficiency when compared to a standard design, as the parameters vary in a specified subset of the parameter space. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering an Emax-sigmoid model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. In a first approach to construct a maximin efficient design we specify a number of possible scenarios, each of which describing a possible shape of the dose-response relation. The design obtained is shown to be at least 15 percent more efficient than the uniform design. It is then shown that the obtained design is maximin efficient also for a much larger parameter set defined by parameter values between those specified by the initial scenarios.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:23:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 275. Miller, Frank PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt1268",{id:"formSmash:items:resultList:24:j_idt1268",widgetVar:"widget_formSmash_items_resultList_24_j_idt1268",onLabel:"Miller, Frank ",offLabel:"Miller, Frank ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt1271",{id:"formSmash:items:resultList:24:j_idt1271",widgetVar:"widget_formSmash_items_resultList_24_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Friede, TimPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Sample size re-estimation and continuous monitoring of the variance in longitudinal trials2014Inngår i: Adaptive Designs & Multiple Testing Procedures, 2014, s. 21-21Konferansepaper (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:24:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_24_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In many clinical trials, frequent longitudinal data is collected from each patient. For example in chronic pain trials, daily pain measurements of the patients can be collected during several weeks which leads to a large number of highly correlated post-baseline measurements for each patient.

Blinded sample size re-estimation or continuous monitoring of the variance (Friede and Miller, 2012) can deal with situations where uncertainty regarding the true variance exists. In trials with longitudinal data, the situation is common that at interim looks a restricted number of patients have completed the study but a large number has started treatment and first post-baseline data is collected but endpoint data is not yet available. Nevertheless, it is reasonable that the partial data available from these patients gives useful information about the variance of the endpoint (Wüst and Kieser, 2003; Wachtlin and Kieser, 2013).

In this talk, we first quantify the gain of including partial data from patients when estimating the variance. Variability of sample size is often reduced but the amount of reduction depends on the correlation between measurements. Then, our main interest is to investigate the usefulness of a parametric model assumption for the covariance structure. We quantify the gain from the model assumption when the assumed model is correct and discuss consequences when a wrong model is assumed.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:24:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 276. Miller, Frank PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt1268",{id:"formSmash:items:resultList:25:j_idt1268",widgetVar:"widget_formSmash_items_resultList_25_j_idt1268",onLabel:"Miller, Frank ",offLabel:"Miller, Frank ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt1271",{id:"formSmash:items:resultList:25:j_idt1271",widgetVar:"widget_formSmash_items_resultList_25_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Zohar, SarahStallard, NigelMadan, JasonPosch, MartinHee, Siew WanPearce, MichaelVågerö, MårtenDay, SimonPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Approaches to sample size calculation for clinical trials in rare diseases2018Inngår i: Pharmaceutical statistics, ISSN 1539-1604, E-ISSN 1539-1612, Vol. 17, nr 3, s. 214-230Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:25:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_25_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:25:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 277. Morlanes, José Igor PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt1268",{id:"formSmash:items:resultList:26:j_idt1268",widgetVar:"widget_formSmash_items_resultList_26_j_idt1268",onLabel:"Morlanes, José Igor ",offLabel:"Morlanes, José Igor ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Some Extensions of Fractional Ornstein-Uhlenbeck Model: Arbitrage and Other Applications2017Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:26:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_26_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This doctoral thesis endeavors to extend probability and statistical models using stochastic differential equations. The described models capture essential features from data that are not explained by classical diffusion models driven by Brownian motion.

New results obtained by the author are presented in five articles. These are divided into two parts. The first part involves three articles on statistical inference and simulation of a family of processes related to fractional Brownian motion and Ornstein-Uhlenbeck process, the so-called fractional Ornstein-Uhlenbeck process of the second kind (fOU

_{2}). In two of the articles, we show how to simulate fOU_{2}by means of*circulant embedding method and memoryless transformations*. In the other one, we construct a least squares consistent estimator of the drift parameter and prove the central limit theorem using techniques from*Stochastic Calculus for Gaussian processes and Malliavin Calculus*.The second phase of my research consists of two articles about jump market models and arbitrage portfolio strategies for an insider trader. One of the articles describes two arbitrage free markets according to their risk neutral valuation formula and an arbitrage strategy by switching the markets. The key aspect is the difference in volatility between the markets. Statistical evidence of this situation is shown from a sequential data set. In the other one, we analyze the arbitrage strategies of an strong insider in a pure jump Markov chain financial market by means of a likelihood process. This is constructed in an enlarged filtration using

*Itô calculus and general theory of stochastic processes*.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:26:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 278. Morlanes, José Igor PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt1268",{id:"formSmash:items:resultList:27:j_idt1268",widgetVar:"widget_formSmash_items_resultList_27_j_idt1268",onLabel:"Morlanes, José Igor ",offLabel:"Morlanes, José Igor ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt1271",{id:"formSmash:items:resultList:27:j_idt1271",widgetVar:"widget_formSmash_items_resultList_27_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Andreev, AndriyStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Simulation of fractional Ornstein-Uhlenbeck of the second kind by Circulant Embedding method2017Konferansepaper (Fagfellevurdert)279. Morlanes, José Igor PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt1268",{id:"formSmash:items:resultList:28:j_idt1268",widgetVar:"widget_formSmash_items_resultList_28_j_idt1268",onLabel:"Morlanes, José Igor ",offLabel:"Morlanes, José Igor ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt1271",{id:"formSmash:items:resultList:28:j_idt1271",widgetVar:"widget_formSmash_items_resultList_28_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Aalto University, Finland.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Rasila, AnttiSottinen, TommiPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Empirical Evidence on arbitrage by changing the stock exchange2009Inngår i: Advances and Applications in Statistics, ISSN 0972-3617, Vol. 12, nr 2, s. 223-233Artikkel i tidsskrift (Fagfellevurdert)280. Mozayyan, Sina PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt1268",{id:"formSmash:items:resultList:29:j_idt1268",widgetVar:"widget_formSmash_items_resultList_29_j_idt1268",onLabel:"Mozayyan, Sina ",offLabel:"Mozayyan, Sina ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:29:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistisk undersökning av valutakurser: En jämförelse mellan olika prognosmodeller2017Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgaveAbstract [sv] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:29:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_29_j_idt1306_0_j_idt1307",onLabel:"Abstract [sv]",offLabel:"Abstract [sv]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Valutamarknaden är världens största marknad och en nödvändig del av dagens globala samhälle, som gör det möjligt för företag att göra affärer i olika valutor och mellan olika gränser. Marknaden utgör en stor handelsplattform för både små och stora aktörer, för vilka det är viktigt att prognostisera valutakurser med gott resultat. Att modellera finansiella instrument i form av tidsserier är en av de vanligaste investeringsstrategierna och dess användningsområde sträcker sig från valutamarknaden till bland annat aktiemarknaden och råvarumarknaden. I denna uppsats undersöks fyra olika statistiska metoder för att modellera valutakursen Euro-US Dollar givet historisk data, och prognoser görs med de framtagna modellerna. Dessa metoder är slumpvandring, ARIMA, ARIMA-GARCH och VAR. Vidare undersöks för den dynamiska VAR-modellen hur valutamarkanden påverkar, och blir påverkad av, långa och korta räntan. Resultaten visar att ARIMA(3,1,2) förklarar valutakursen bäst medan VAR(2) med valutakursen och skillnaden mellan långa räntor som ingående variabler ger de bästa prediktionerna.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:29:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 281. Muhinyuza, Stanislas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt1268",{id:"formSmash:items:resultList:30:j_idt1268",widgetVar:"widget_formSmash_items_resultList_30_j_idt1268",onLabel:"Muhinyuza, Stanislas ",offLabel:"Muhinyuza, Stanislas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:30:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical Methods in Portfolio Theory2018Licentiatavhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:30:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_30_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this thesis we develop new statistical theory and apply it to practical problems dealing with mean-variance optimal portfolio selection. More precisely, we derive an exact statistical test for the characterization of the location of the tangency portfolio (TP) on the efficient frontier. Since the construction of the TP involves the product of an (inverse) Wishart matrix and a normal vector, we also study the distributional properties of functions involving such a product. The first paper focuses on the determination of the existence of the TP. Due to problem of parameter uncertainty, specifying the location of the TP on the set of feasible portfolio becomes a difficult task. Assuming that the asset returns are independent and multivariate normally distributed, we propose a finite-sample test on mean-variance efficiency of the TP. We derive the distribution of the proposed test statistic under both hypotheses, using which we assess the power of the test and construct a confidence interval. Furthermore, we conduct the out-of sample performance of the portfolio determined by implementing the proposed test. Through an extensive simulation we show the robustness of the new test towards the violation of the normality assumption. In an empirical study we apply the developed theory to real data.In the second paper we derive a stochastic representation of the product of a singular Wishart matrix and a singular Gaussian vector. The derived stochastic representation is then used to obtain the characteristic function of that product and to prove the asymptotic normality under double asymptotic regime. Moreover, the derived stochastic representation gives an efficient way of how the elements of the product should be simulated. A simulation study shows a good performance of the obtained asymptotic distribution.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:30:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 282. Munkhammar, Joakim et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt1271",{id:"formSmash:items:resultList:31:j_idt1271",widgetVar:"widget_formSmash_items_resultList_31_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mattsson, LarsStockholms universitet, Nordiska institutet för teoretisk fysik (Nordita).Rydén, JesperPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Polynomial probability distribution estimation using the method of moments2017Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, nr 4, artikkel-id e0174573Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:31:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_31_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We suggest a procedure for estimating Nth degree polynomial approximations to unknown (or known) probability density functions (PDFs) based on N statistical moments from each distribution. The procedure is based on the method of moments and is setup algorithmically to aid applicability and to ensure rigor in use. In order to show applicability, polynomial PDF approximations are obtained for the distribution families Normal, Log-Normal, Weibull as well as for a bimodal Weibull distribution and a data set of anonymized household electricity use. The results are compared with results for traditional PDF series expansion methods of Gram-Charlier type. It is concluded that this procedure is a comparatively simple procedure that could be used when traditional distribution families are not applicable or when polynomial expansions of probability distributions might be considered useful approximations. In particular this approach is practical for calculating convolutions of distributions, since such operations become integrals of polynomial expressions. Finally, in order to show an advanced applicability of the method, it is shown to be useful for approximating solutions to the Smoluchowski equation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:31:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 283. Nahtman, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt1268",{id:"formSmash:items:resultList:32:j_idt1268",widgetVar:"widget_formSmash_items_resultList_32_j_idt1268",onLabel:"Nahtman, Tatjana ",offLabel:"Nahtman, Tatjana ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:32:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Permutation invariance and reparameterizations in linear models2004Doktoravhandling, monografi (Annet vitenskapelig)284. Nahtman, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt1268",{id:"formSmash:items:resultList:33:j_idt1268",widgetVar:"widget_formSmash_items_resultList_33_j_idt1268",onLabel:"Nahtman, Tatjana ",offLabel:"Nahtman, Tatjana ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt1271",{id:"formSmash:items:resultList:33:j_idt1271",widgetVar:"widget_formSmash_items_resultList_33_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); On a Class of Singular Nonsymmetric Matrices with Nonnegative Integer Spectra2007Rapport (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:33:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_33_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The objective of this paper is to consider a class of singular nonsym

metric matrices with integer spectrum. The class comprises generalized triangular matrices with diagonal elements obtained by summing the elements of the corresponding column. If the size of a matrix belonging to the class equals $n\times n$, the spectrum of the matrix is given by the sequence of distinct non-negative integers up to n-1, irrespective of the elements of the matrix. Right and left eigenvectors are obtained. Moreover, several interesting relations are presented, including factorizations via triangular matrices.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:33:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 285. Nahtman, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt1268",{id:"formSmash:items:resultList:34:j_idt1268",widgetVar:"widget_formSmash_items_resultList_34_j_idt1268",onLabel:"Nahtman, Tatjana ",offLabel:"Nahtman, Tatjana ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt1271",{id:"formSmash:items:resultList:34:j_idt1271",widgetVar:"widget_formSmash_items_resultList_34_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Shift Permutation Invariance in Linear Random Factor Models2008Inngår i: Mathematical Methods of Statistics, ISSN 1066-5307, Vol. 17, nr 2, s. 173-185Artikkel i tidsskrift (Fagfellevurdert)286. Nellåker, Christoffer et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt1271",{id:"formSmash:items:resultList:35:j_idt1271",widgetVar:"widget_formSmash_items_resultList_35_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Uhrzander, FredrikTyrcha, JoannaStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Matematisk statistik.Karlsson, HåkanPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mixture models for analysis of melting temperature data2008Inngår i: BMC Bioinformatics, Vol. 9:370Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:35:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_35_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Background

In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.

Results

Mixture model analysis was applied to Tm data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.

Conclusion

Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data to be analyzed, classified, and compared in an unbiased manner.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:35:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 287. Neumann, André et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt1271",{id:"formSmash:items:resultList:36:j_idt1271",widgetVar:"widget_formSmash_items_resultList_36_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bodnar, TarasStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.Pfeifer, DietmarDickhaus, ThorstenPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Multivariate multiple test procedures based on nonparametric copula estimation2019Inngår i: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 61, nr 1, s. 40-61Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:36:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_36_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Multivariate multiple test procedures have received growing attention recently. This is due to the fact that data generated by modern applications typically are highdimensional, but possess pronounced dependencies due to the technical mechanisms involved in the experiments. Hence, it is possible and often necessary to exploit these dependencies in order to achieve reasonable power. In the present paper, we express dependency structures in the most general manner, namely, by means of copula functions. One class of nonparametric copula estimators is constituted by Bernstein copulae. We extend previous statistical results regarding bivariate Bernstein copulae to the multivariate case and study their impact on multiple tests. In particular, we utilize them to derive asymptotic confidence regions for the family-wise error rate (FWER) of multiple test procedures that are empirically calibrated by making use of Bernstein copulae approximations of the dependency structure among the test statistics. This extends a similar approach by Stange et al. (2015) in the parametric case. A simulation study quantifies the gain in FWER level exhaustion and, consequently, power that can be achieved by exploiting the dependencies, in comparison with common threshold calibrations like the Bonferroni or Šidák corrections. Finally, we demonstrate an application of the proposed methodology to real-life data from insurance.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:36:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 288. Nordvall Lagerås, Andreas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt1268",{id:"formSmash:items:resultList:37:j_idt1268",widgetVar:"widget_formSmash_items_resultList_37_j_idt1268",onLabel:"Nordvall Lagerås, Andreas ",offLabel:"Nordvall Lagerås, Andreas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Markov Chains, Renewal, Branching and Coalescent Processes: Four Topics in Probability Theory2007Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:37:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_37_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis consists of four papers.

In paper 1, we prove central limit theorems for Markov chains under (local) contraction conditions. As a corollary we obtain a central limit theorem for Markov chains associated with iterated function systems with contractive maps and place-dependent Dini-continuous probabilities.

In paper 2, properties of inverse subordinators are investigated, in particular similarities with renewal processes. The main tool is a theorem on processes that are both renewal and Cox processes.

In paper 3, distributional properties of supercritical and especially immortal branching processes are derived. The marginal distributions of immortal branching processes are found to be compound geometric.

In paper 4, a description of a dynamic population model is presented, such that samples from the population have genealogies as given by a Lambda-coalescent with mutations. Depending on whether the sample is grouped according to litters or families, the sampling distribution is either regenerative or non-regenerative.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:37:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 289. Normark, Sofia PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt1268",{id:"formSmash:items:resultList:38:j_idt1268",widgetVar:"widget_formSmash_items_resultList_38_j_idt1268",onLabel:"Normark, Sofia ",offLabel:"Normark, Sofia ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Minimax designs for 2(k) factorial experiments for generalized linear models2016Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 45, nr 16, s. 4788-4797Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:38:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_38_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Formulas for A- and C-optimal allocations for binary factorial experiments in the context of generalized linear models are derived. Since the optimal allocations depend on GLM weights, which often are unknown, a minimax strategy is considered. This is shown to be simple to apply to factorial experiments. Efficiency is used to evaluate the resulting design. In some cases, the minimax design equals the optimal design. For other cases no general conclusion can be drawn. An example of a two-factor logit model suggests that the minimax design performs well, and often better than a uniform allocation.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:38:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 290. Normark, Sofia PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt1268",{id:"formSmash:items:resultList:39:j_idt1268",widgetVar:"widget_formSmash_items_resultList_39_j_idt1268",onLabel:"Normark, Sofia ",offLabel:"Normark, Sofia ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Topics in optimal design of experiments2013Licentiatavhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:39:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_39_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis contributes to optimal design of experiments in situations where the optimal design depends on unknown parameters. Such situations often occur when the response variable does not have normal distribution, as is the case with many generalized linear models (GLM) and many models for binary response. In these cases the experimenter may either have to guess the values on the parameters and use a locally optimal design or use some method to ensure that the experiment will not yield parameter estimates with too large variance.

The thesis consists of two papers: In Paper I, optimal allocation to treatment groups for binary factorial experiments is studied. If the response variable is from the exponential family of distributions, and hence can be modeled by a GLM, the optimal allocation depends on the GLM weights, that in general are unknown. For this problem, a minimax design is considered. Under some conditions, the minimax allocation is as efficient as an optimal design. For other cases, no general conclusions can be drawn but an example suggests that the efficiency loss is small, compared to an optimal design.

Paper II examines the efficiency of locally optimal designs for a class of binary response models where the optimal design depends on values of unknown parameters. It is believed that if the parameter values that are used to construct the locally optimal design are close to the correct values, the resulting design may be “close to optimal”. An approximation of the efficiency by a Taylor function is given. This function may be used to asses how far from the true values the guessed parameters may be, in order for the design to attain a minimum efficiency.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:39:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 291. Norén, G. Niklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt1268",{id:"formSmash:items:resultList:40:j_idt1268",widgetVar:"widget_formSmash_items_resultList_40_j_idt1268",onLabel:"Norén, G. Niklas ",offLabel:"Norén, G. Niklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:40:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical methods for knowledge discovery in adverse drug reaction surveillance2007Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:40:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_40_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Collections of individual case safety reports are the main resource for early discovery of unknown adverse reactions to drugs once they have been introduced to the general public. The data sets involved are complex and based on voluntary submission of reports, but contain pieces of very important information. The aim of this thesis is to propose computationally feasible statistical methods for large-scale knowledge discovery in these data sets. The main contributions are a duplicate detection method that can reliably identify pairs of unexpectedly similar reports and a new measure for highlighting suspected drug-drug interaction.

Specifically, we extend the hit-miss model for database record matching with a hit-miss mixture model for scoring numerical record fields and a new method to compensate for strong record field correlations. The extended hit-miss model is implemented for the WHO database and demonstrated to be useful in real world duplicate detection, despite the noisy and incomplete information on individual case safety reports. The Information Component measure of disproportionality has been in routine use since 1998 to screen the WHO database for excessive adverse drug reaction reporting rates. Here, it is further refined. We introduce improved credibility intervals for rare events, post-stratification adjustment for suspected confounders and an extension to higher order associations that allows for simple but robust screening for potential risk factors. A new approach to identifying reporting patterns indicative of drug-drug interaction is also proposed. Finally, we describe how imprecision estimates specific to each prediction of a Bayes classifier may be obtained with the Bayesian bootstrap. Such case-based imprecision estimates allow for better prediction when different types of errors have different associated loss, with a possible application in combining quantitative and clinical filters to highlight drug-ADR pairs for clinical review.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:40:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 292. Norén, G. Niklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt1268",{id:"formSmash:items:resultList:41:j_idt1268",widgetVar:"widget_formSmash_items_resultList_41_j_idt1268",onLabel:"Norén, G. Niklas ",offLabel:"Norén, G. Niklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt1271",{id:"formSmash:items:resultList:41:j_idt1271",widgetVar:"widget_formSmash_items_resultList_41_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hopstadius, JohanBate, AndrewPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery2013Inngår i: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 22, nr 1, s. 57-69Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:41:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_41_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Large observational data sets are a great asset to better understand the effects of medicines in clinical practice and, ultimately, improve patient care. For an empirical pattern in observational data to be of practical relevance, it should represent a substantial deviation from the null model. For the purpose of identifying such deviations, statistical significance tests are inadequate, as they do not on their own distinguish the magnitude of an effect from its data support. The observed-to-expected (OE) ratio on the other hand directly measures strength of association and is an intuitive basis to identify a range of patterns related to event rates, including pairwise associations, higher order interactions and temporal associations between events over time. It is sensitive to random fluctuations for rare events with low expected counts but statistical shrinkage can protect against spurious associations. Shrinkage OE ratios provide a simple but powerful framework for large-scale pattern discovery. In this article, we outline a range of patterns that are naturally viewed in terms of OE ratios and propose a straightforward and effective statistical shrinkage transformation that can be applied to any such ratio. The proposed approach retains emphasis on the practical relevance and transparency of highlighted patterns, while protecting against spurious associations.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:41:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 293. Nyimvua Mbare, Shaban PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt1268",{id:"formSmash:items:resultList:42:j_idt1268",widgetVar:"widget_formSmash_items_resultList_42_j_idt1268",onLabel:"Nyimvua Mbare, Shaban ",offLabel:"Nyimvua Mbare, Shaban ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Epidemics on networks and early stage vaccination2007Licentiatavhandling, monografi (Annet vitenskapelig)294. Ogenstad, Stephan PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt1268",{id:"formSmash:items:resultList:43:j_idt1268",widgetVar:"widget_formSmash_items_resultList_43_j_idt1268",onLabel:"Ogenstad, Stephan ",offLabel:"Ogenstad, Stephan ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical analysis of censored survival time data in clinical trials1982Doktoravhandling, med artikler (Annet vitenskapelig)295. Ohlsson, Esbjörn PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt1268",{id:"formSmash:items:resultList:44:j_idt1268",widgetVar:"widget_formSmash_items_resultList_44_j_idt1268",onLabel:"Ohlsson, Esbjörn ",offLabel:"Ohlsson, Esbjörn ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt1271",{id:"formSmash:items:resultList:44:j_idt1271",widgetVar:"widget_formSmash_items_resultList_44_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Matematisk statistik.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Lauzeningks, JanPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The one-year non-life insurance risk2008Konferansepaper (Annet (populærvitenskap, debatt, mm))Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:44:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_44_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); With few exceptions, the literature on non-life insurance reserve risk has been devoted to the ultimo risk, the risk in the full run-off of the liabilities. This is in contrast to the short time horizon in models for the total risk of the insurance company, and in particular the one-year risk perspective taken in the Solvency II project, and in the computation of risk margins with the Cost-of-Capital method. This paper aims at clarifying the methodology for the one-year risk; in particular we describe a simulation approach to the one-year reserve risk. We also discuss the one-year premium risk and the premium reserve. Finally, we initiate a discussion on the role of risk margins and discounting for the reserve and premium risk.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:44:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 296. Olsson, Fredrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt1268",{id:"formSmash:items:resultList:45:j_idt1268",widgetVar:"widget_formSmash_items_resultList_45_j_idt1268",onLabel:"Olsson, Fredrik ",offLabel:"Olsson, Fredrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Inbreeding, Effective Population Sizes and Genetic Differentiation: A Mathematical Analysis of Structured Populations2015Doktoravhandling, med artikler (Annet vitenskapelig)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:45:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_45_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis consists of four papers on various aspects of inbreeding, effective population sizes and genetic differentiation in structured populations, that is, populations that consist of a number of subpopulations. Three of the papers concern age structured populations, where in the first paper we concentrate on calculating the variance effective population size (

*N*) and how_{eV}*N*depends on the time between measurements and the weighting scheme of age classes. In the third paper we develop an estimation procedure of_{eV}*N*which uses age specific demographic parameters to obtain approximately unbiased estimates. A simulation method for age structured populations is presented in the fourth paper. It is applicable to models with multiallelic loci in linkage equilibrium._{eV}In the second paper, we develop a framework for analysis of effective population sizes and genetic differentiation in geographically subdivided populations with a general migration scheme. Predictions of gene identities and gene diversities of the population are presented, which are used to find expressions for effective population sizes (

*N*) and the coefficient of gene differentiation (_{e}*G*). We argue that not only the asymptotic values of_{ST}*N*and_{e}*G*are important, but also their temporal dynamic patterns._{ST}The models presented in this thesis are important for understanding how different age decomposition, migration and reproduction scenarios of a structured population affect quantities, such as various types of effective sizes and genetic differentiation between subpopulations.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:45:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 297. Olsson, Fredrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt1268",{id:"formSmash:items:resultList:46:j_idt1268",widgetVar:"widget_formSmash_items_resultList_46_j_idt1268",onLabel:"Olsson, Fredrik ",offLabel:"Olsson, Fredrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt1271",{id:"formSmash:items:resultList:46:j_idt1271",widgetVar:"widget_formSmash_items_resultList_46_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hössjer, OlaStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation of the variance effective population size in age structured populations2015Inngår i: Theoretical Population Biology, ISSN 0040-5809, E-ISSN 1096-0325, Vol. 101, s. 9-23Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:46:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_46_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The variance effective population size for age structured populations is generally hard to estimate and the temporal method often gives biased estimates. Here, we give an explicit expression for a correction factor which, combined with estimates from the temporal method, yield approximately unbiased estimates. The calculation of the correction factor requires knowledge of the age specific offspring distribution and survival probabilities as well as possible correlation between survival and reproductive success. In order to relax these requirements, we show that only first order moments of these distributions need to be known if the time between samples is large, or individuals from all age classes which reproduce are sampled. A very explicit approximate expression for the asymptotic coefficient of standard deviation of the estimator is derived, and it can be used to construct confidence intervals and optimal ways of weighting information from different markers. The asymptotic coefficient of standard deviation can also be used to design studies and we show that in order to maximize the precision for a given sample size, individuals from older age classes should be sampled since their expected variance of allele frequency change is higher and easier to estimate. However, for populations with fluctuating age class sizes, the accuracy of the method is reduced when samples are taken from older age classes with high demographic variation. We also present a method for simultaneous estimation of the variance effective and census population size.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:46:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 298. Olsson, Fredrik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt1268",{id:"formSmash:items:resultList:47:j_idt1268",widgetVar:"widget_formSmash_items_resultList_47_j_idt1268",onLabel:"Olsson, Fredrik ",offLabel:"Olsson, Fredrik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt1271",{id:"formSmash:items:resultList:47:j_idt1271",widgetVar:"widget_formSmash_items_resultList_47_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hössjer, OlaStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Simulation methods for age structured populationsInngår i: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134Artikkel i tidsskrift (Fagfellevurdert)299. Ondra, Thomas et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt1271",{id:"formSmash:items:resultList:48:j_idt1271",widgetVar:"widget_formSmash_items_resultList_48_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Dmitrienko, AlexFriede, TimGraf, AlexandraMiller, FrankStockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.Stallard, NigelPosch, MartinPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review2016Inngår i: Journal of Biopharmaceutical Statistics, ISSN 1054-3406, E-ISSN 1520-5711, Vol. 26, nr 1, s. 99-119Artikkel i tidsskrift (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:48:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_48_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:48:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 300. Pavlenko, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt1268",{id:"formSmash:items:resultList:49:j_idt1268",widgetVar:"widget_formSmash_items_resultList_49_j_idt1268",onLabel:"Pavlenko, Tatjana ",offLabel:"Pavlenko, Tatjana ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt1271",{id:"formSmash:items:resultList:49:j_idt1271",widgetVar:"widget_formSmash_items_resultList_49_j_idt1271",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Björkström, AndersStockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Exploiting Sparse Dependence Structure in Model Based Classification2010Inngår i: Combining Soft Computing and Statistical Methods in Data Analysis / [ed] Christian Borgelt, Berlin: Springer , 2010, s. 509-517Konferansepaper (Fagfellevurdert)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt1306_0_j_idt1307",{id:"formSmash:items:resultList:49:j_idt1306:0:j_idt1307",widgetVar:"widget_formSmash_items_resultList_49_j_idt1306_0_j_idt1307",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Sparsity patterns discovered in the data dependence structure were used to reduce the dimensionality and improve performance accuracy of the model based classifier in a high dimensional framework.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:49:j_idt1306:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500});

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