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251. Pavlenko, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt584",{id:"formSmash:items:resultList:0:j_idt584",widgetVar:"widget_formSmash_items_resultList_0_j_idt584",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_0_j_idt587",{id:"formSmash:items:resultList:0:j_idt587",widgetVar:"widget_formSmash_items_resultList_0_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Björkström, AndersStockholm University, Faculty of Science, Department of Mathematics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:0:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Exploiting Sparse Dependence Structure in Model Based Classification2010In: Combining Soft Computing and Statistical Methods in Data Analysis / [ed] Christian Borgelt, Berlin: Springer , 2010, p. 509-517Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_0_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:0:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_0_j_idt622_0_j_idt623",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:0:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 252. Pavlenko, Tatjana et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt587",{id:"formSmash:items:resultList:1:j_idt587",widgetVar:"widget_formSmash_items_resultList_1_j_idt587",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:1:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Björkström, AndersStockholm University, Faculty of Science, Department of Mathematics.Tillander, AnnikaStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:1:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Covariance structure approximation via glasso in high dimensional supervised classification2012In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 39, no 8, p. 1643-1666Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_1_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:1:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_1_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Recent work has shown that the Lasso-based regularization is very useful for estimating the high-dimensional inverse covariance matrix. A particularly useful scheme is based on penalizing the l(1) norm of the off-diagonal elements to encourage sparsity. We embed this type of regularization into high-dimensional classification. A two-stage estimation procedure is proposed which first recovers structural zeros of the inverse covariance matrix and then enforces block sparsity by moving non-zeros closer to the main diagonal. We show that the block-diagonal approximation of the inverse covariance matrix leads to an additive classifier, and demonstrate that accounting for the structure can yield better performance accuracy. Effect of the block size on classification is explored, and a class of as ymptotically equivalent structure approximations in a high-dimensional setting is specified. We suggest a variable selection at the block level and investigate properties of this procedure in growing dimension asymptotics. We present a consistency result on the feature selection procedure, establish asymptotic lower an upper bounds for the fraction of separative blocks and specify constraints under which the reliable classification with block-wise feature selection can be performed. The relevance and benefits of the proposed approach are illustrated on both simulated and real data.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:1:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 253. Pavlenko, Tatjana PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt584",{id:"formSmash:items:resultList:2:j_idt584",widgetVar:"widget_formSmash_items_resultList_2_j_idt584",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_2_j_idt587",{id:"formSmash:items:resultList:2:j_idt587",widgetVar:"widget_formSmash_items_resultList_2_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Chernyak, OleksandrPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:2:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Credit Risk Modeling Using Bayesian Networks2010In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 25, no 4, p. 326-344Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_2_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:2:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_2_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The main goal of this research is to demonstrate how probabilistic graphs may be used for modeling and assessment of credit concentration risk. The destructive power of credit concentrations essentially depends on the amount of correlation among borrowers. However, borrower companies correlation and concentration of credit risk exposures have been difficult for the banking industry to measure in an objective way as they are riddled with uncertainty. As a result, banks do not manage to make a quantitative link to the correlation driving risks and fail to prevent concentrations from accumulating. In this paper, we argue that Bayesian networks provide an attractive solution to these problems and we show how to apply them in representing, quantifying and managing the uncertain knowledge in concentration of credits risk exposures. We suggest the stepwise Bayesian network model building and show how to incorporate expert-based prior beliefs on the risk exposure of a group of related borrowers, and then update these beliefs through the whole model with the new information. We then explore a specific graph structure, a tree-augmented Bayesian network and show that this model provides better understanding of the risk accumulating due to business links between borrowers. We also present two strategies of model assessment that exploit the measure of mutual information and show that the constructed Bayesian network is a reliable model that can be implemented to identify and control threat from concentration of credit exposures. Finally, we demonstrate that suggested tree-augmented Bayesian network is also suitable for stress-testing analysis, in particular, it can provide the estimates of the posterior risk of losses related to the unfavorable changes in the financial conditions of a group of related borrowers.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:2:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 254. Pearce, Michael et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt587",{id:"formSmash:items:resultList:3:j_idt587",widgetVar:"widget_formSmash_items_resultList_3_j_idt587",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:3:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Hee, Siew WanMadan, JasonPosch, MartinDay, SimonMiller, FrankStockholm University, Faculty of Social Sciences, Department of Statistics.Zohar, SarahStallard, NigelPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:3:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Value of information methods to design a clinical trial in a small population to optimise a health economic utility function2018In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 18, article id 20Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_3_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:3:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_3_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Background: Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. Methods: We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. Results: The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. Conclusions: Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:3:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 255. Peristera, Paraskevi PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt584",{id:"formSmash:items:resultList:4:j_idt584",widgetVar:"widget_formSmash_items_resultList_4_j_idt584",onLabel:"Peristera, Paraskevi ",offLabel:"Peristera, Paraskevi ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt587",{id:"formSmash:items:resultList:4:j_idt587",widgetVar:"widget_formSmash_items_resultList_4_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Stress Research Institute.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Ghilagaber, GebrenegusStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:4:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Another Look at the Relationship between Cohabitation and Marriage: The Use of Crude and Net Probabilities2017In: Bulletin of Sociological Methodology, ISSN 0759-1063, E-ISSN 2070-2779, Vol. 136, p. 66-73Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_4_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:4:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_4_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this paper we propose an approach based on the theoretical relationship betweencrude and net probabilities of marriage in a competing-risk framework. Analyses basedon family dynamics among Swedish men born 1936-1964 show that the probabilities ofmarriage increase if cohabitation was eliminated (and that probabilities of cohabitationincrease if marriage was eliminated). Further, the gains in net probabilities increase at the prime ages of family formation (20-28) but are less significant at other ages. Such resultssupport (at least for the data at hand) the argument that informal cohabitation serves as aprelude to marriage rather than a permanent replacement to it.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:4:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 256. Peristera, Paraskevi PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt584",{id:"formSmash:items:resultList:5:j_idt584",widgetVar:"widget_formSmash_items_resultList_5_j_idt584",onLabel:"Peristera, Paraskevi ",offLabel:"Peristera, Paraskevi ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_5_j_idt587",{id:"formSmash:items:resultList:5:j_idt587",widgetVar:"widget_formSmash_items_resultList_5_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Ghilagaber, GebrenegusStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:5:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimating Density and Hazard Functions through Kernel and Wavelet Smoothers2012Conference paper (Other academic)257. Peristera, Paraskevi PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt584",{id:"formSmash:items:resultList:6:j_idt584",widgetVar:"widget_formSmash_items_resultList_6_j_idt584",onLabel:"Peristera, Paraskevi ",offLabel:"Peristera, Paraskevi ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_6_j_idt587",{id:"formSmash:items:resultList:6:j_idt587",widgetVar:"widget_formSmash_items_resultList_6_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Kostaki, AnastasiaAthens University of Economics and Business.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:6:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Wavelet estimates for graduating demographicr rates2012Conference paper (Other academic)258. Pettersson, Nicklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt584",{id:"formSmash:items:resultList:7:j_idt584",widgetVar:"widget_formSmash_items_resultList_7_j_idt584",onLabel:"Pettersson, Nicklas ",offLabel:"Pettersson, Nicklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:7:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Bias Reduction Of Finite Population Imputation By Kernel MethodsIn: Statistics in Transition, ISSN 1234-7655Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:7:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_7_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Missing data is a nuisance in statistics. Real donor imputation can be used with item nonresponse. A pool of donor units with similar values on auxiliary variables is matched to each unit with missing values. The missing value is then replaced by a copy of the corresponding observed value from a randomly drawn donor. Such methods can to some extent protect against nonresponse bias. But bias also depends on the estimator and the nature of the data. We adopt techniques from kernel estimation to combat this bias. Motivated by Pólya urn sampling, we sequentially update the set of potential donors with units already imputed, and use multiple imputations via Bayesian bootstrap to account for imputation uncertainty. Simulations with a single auxiliary variable show that our imputation method performs almost as well as competing methods with linear data, but better when data is nonlinear, especially with large samples.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:7:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 259. Pettersson, Nicklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_8_j_idt584",{id:"formSmash:items:resultList:8:j_idt584",widgetVar:"widget_formSmash_items_resultList_8_j_idt584",onLabel:"Pettersson, Nicklas ",offLabel:"Pettersson, Nicklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Informed kernel imputationArticle in journal (Refereed)260. Pettersson, Nicklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_9_j_idt584",{id:"formSmash:items:resultList:9:j_idt584",widgetVar:"widget_formSmash_items_resultList_9_j_idt584",onLabel:"Pettersson, Nicklas ",offLabel:"Pettersson, Nicklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Kernel imputation with multivariate auxiliariesArticle in journal (Refereed)261. Pettersson, Nicklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt584",{id:"formSmash:items:resultList:10:j_idt584",widgetVar:"widget_formSmash_items_resultList_10_j_idt584",onLabel:"Pettersson, Nicklas ",offLabel:"Pettersson, Nicklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Multiple Kernel Imputation: A Locally Balanced Real Donor Method2013Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:10:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_10_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We present an algorithm for imputation of incomplete datasets based on Bayesian exchangeability through Pólya sampling. Each (donee) unit with a missing value is imputed multiple times by observed (real) values on units from a donor pool. The donor pools are constructed using auxiliary variables. Several features from kernel estimation are used to counteract unbalances that are due to sparse and bounded data. Three balancing features can be used with only one single continuous auxiliary variable, but an additional fourth feature need, multiple continuous auxiliary variables. They mainly contribute by reducing nonresponse bias. We examine how the donor pool size should be determined, that is the number of potential donors within the pool. External information is shown to be easily incorporated in the imputation algorithm. Our simulation studies show that with a study variable which can be seen as a function of one or two continuous auxiliaries plus residual noise, the method performs as well or almost as well as competing methods when the function is linear, but usually much better when the function is nonlinear.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:10:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 262. Pettersson, Nicklas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt584",{id:"formSmash:items:resultList:11:j_idt584",widgetVar:"widget_formSmash_items_resultList_11_j_idt584",onLabel:"Pettersson, Nicklas ",offLabel:"Pettersson, Nicklas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:11:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Real donor imputation pools2012In: Proceedings of the Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012 / [ed] Mārtiņš Liberts, Valmiera, 2012, p. 162-168Conference paper (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:11:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_11_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Real donor matching is associated with hot deck imputation. Aux-iliary variables are used to match donee units with missing values to aset of donor units with observed values, and the donee missing valuesare ‘replaced’ by copies of the donor values, as to create completelyfilled in datasets. The matching of donees and donors is complicatedby the fact that the observed sample survey data is often both sparseand bounded. The important choice of how many possible donors tochoose from involves a trade-off between bias and variance. We trans-fer concepts from kernel estimators to real donor imputation. In asimulation study we show how bias, variance and the estimated vari-ance of a population behaves, focusing on the size of donor pools.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:11:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 263. Pielaszkiewicz, Jolanta PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt584",{id:"formSmash:items:resultList:12:j_idt584",widgetVar:"widget_formSmash_items_resultList_12_j_idt584",onLabel:"Pielaszkiewicz, Jolanta ",offLabel:"Pielaszkiewicz, Jolanta ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt587",{id:"formSmash:items:resultList:12:j_idt587",widgetVar:"widget_formSmash_items_resultList_12_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Linnaeus University, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Holgersson, ThomasPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:12:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Mixtures of traces of Wishart and inverse Wishart matrices2019In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_12_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:12:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_12_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Traces of Wishart matrices appear in many applications, for example in finance, discriminant analysis, Mahalanobis distances and angles, loss functions and many more. These applications typically involve mixtures of traces of Wishart and inverse Wishart matrices that are concerned in this paper. Of particular interest are the sampling moments and their limiting joint distribution. The covariance matrix of the marginal positive and negative spectral moments is derived in closed form (covariance matrix of where ). The results are obtained through convenient recursive formulas for and Moreover, we derive an explicit central limit theorem for the scaled vector Y, when and present a simulation study on the convergence to normality and on a skewness measure.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:12:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 264. Posch, Martin et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt587",{id:"formSmash:items:resultList:13:j_idt587",widgetVar:"widget_formSmash_items_resultList_13_j_idt587",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:13:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Klinglmueller, FlorianKönig, FranzMiller, FrankStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:13:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation after blinded sample size reassessment2018In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 27, no 6, p. 1830-1846Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_13_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:13:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_13_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the

*z*-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:13:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 265. Quiroz, Matias PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt584",{id:"formSmash:items:resultList:14:j_idt584",widgetVar:"widget_formSmash_items_resultList_14_j_idt584",onLabel:"Quiroz, Matias ",offLabel:"Quiroz, Matias ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Bayesian Inference in Large Data Problems2015Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_14_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:14:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_14_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges traditional modeling and inference methodology.

This thesis is devoted to developing inference and modeling tools to handle large data sets. Four included papers treat various important aspects of this topic, with a special emphasis on Bayesian inference by scalable Markov Chain Monte Carlo (MCMC) methods.

In the first paper, we propose a novel mixture-of-experts model for longitudinal data. The model and inference methodology allows for manageable computations with a large number of subjects. The model dramatically improves the out-of-sample predictive density forecasts compared to existing models.

The second paper aims at developing a scalable MCMC algorithm. Ideas from the survey sampling literature are used to estimate the likelihood on a random subset of data. The likelihood estimate is used within the pseudomarginal MCMC framework and we develop a theoretical framework for such algorithms based on subsets of the data.

The third paper further develops the ideas introduced in the second paper. We introduce the difference estimator in this framework and modify the methods for estimating the likelihood on a random subset of data. This results in scalable inference for a wider class of models.

Finally, the fourth paper brings the survey sampling tools for estimating the likelihood developed in the thesis into the delayed acceptance MCMC framework. We compare to an existing approach in the literature and document promising results for our algorithm.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:14:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 266. Quiroz, Matias PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt584",{id:"formSmash:items:resultList:15:j_idt584",widgetVar:"widget_formSmash_items_resultList_15_j_idt584",onLabel:"Quiroz, Matias ",offLabel:"Quiroz, Matias ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Sveriges Riksbank, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:15:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Speeding up MCMC by delayed acceptance and data subsamplingManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_15_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:15:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_15_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The complexity of Markov Chain Monte Carlo (MCMC) algorithms arises from the requirement of a likelihood evaluation for the full data set in each iteration. Payne and Mallick (2014) propose to speed up the Metropolis-Hastings algorithm by a delayed acceptance approach where the acceptance decision proceeds in two stages. In the first stage, an estimate of the likelihood based on a random subsample determines if it is likely that the draw will be accepted and, if so, the second stage uses the full data likelihood to decide upon final acceptance. Evaluating the full data likelihood is thus avoided for draws that are unlikely to be accepted. We propose a more precise likelihood estimator which incorporates auxiliary information about the full data likelihood while only operating on a sparse set of the data. It is proved that the resulting delayed acceptance MCMC is asymptotically more efficient compared to that of Payne and Mallick (2014). Furthermore, we adapt the method to handle data sets that are too large to fit in Random-Access Memory (RAM). This adaptation results in an algorithm that samples from an approximate posterior with well studied theoretical properties in the literature.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:15:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 267. Quiroz, Matias PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt584",{id:"formSmash:items:resultList:16:j_idt584",widgetVar:"widget_formSmash_items_resultList_16_j_idt584",onLabel:"Quiroz, Matias ",offLabel:"Quiroz, Matias ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt587",{id:"formSmash:items:resultList:16:j_idt587",widgetVar:"widget_formSmash_items_resultList_16_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Sveriges Riksbank, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Villani, MattiasPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:16:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Dynamic mixture-of-experts models for longitudinal and discrete-time survival dataManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_16_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:16:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_16_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We propose a general class of flexible models for longitudinal data with special emphasis on discrete-time survival data. The model is a finite mixture model where the subjects are allowed to move between components through time. The time-varying probabilities of component memberships are modeled as a function of subject-specific time-varying covariates. This allows for interesting within-subject dynamics and manageable computations even with a large number of subjects. Each parameter in the component densities and in the mixing function is connected to its own set of covariates through a link function. The models are estimated using a Bayesian approach via a highly efficient Markov Chain Monte Carlo (MCMC) algorithm with tailored proposals and variable selection in all sets of covariates. The focus of the paper is on models for discrete-time survival data with an application to bankruptcy prediction for Swedish firms, using both exponential and Weibull mixture components. The dynamic mixture-of-experts models are shown to have an interesting interpretation and to dramatically improve the out-of-sample predictive density forecasts compared to models with time-invariant mixture probabilities.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:16:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 268. Quiroz, Matias PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt584",{id:"formSmash:items:resultList:17:j_idt584",widgetVar:"widget_formSmash_items_resultList_17_j_idt584",onLabel:"Quiroz, Matias ",offLabel:"Quiroz, Matias ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt587",{id:"formSmash:items:resultList:17:j_idt587",widgetVar:"widget_formSmash_items_resultList_17_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Villani, MattiasKohn, RobertPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:17:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Scalable MCMC for large data problems using data subsampling and the difference estimatorManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_17_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:17:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_17_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for data sets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using only a small fraction of the data. Our algorithm improves on the O(n) complexity of regular MCMC by operating over local data clusters instead of the full sample when computing the likelihood. The likelihood estimate is used in a Pseudo-marginal framework to sample from a perturbed posterior which is within O(m

^{-1/2}) of the true posterior, where m is the subsample size. The method is applied to a logistic regression model to predict firm bankruptcy for a large data set. We document a significant speed up in comparison to the standard MCMC on the full data set.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:17:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 269. Quiroz, Matias PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt584",{id:"formSmash:items:resultList:18:j_idt584",widgetVar:"widget_formSmash_items_resultList_18_j_idt584",onLabel:"Quiroz, Matias ",offLabel:"Quiroz, Matias ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt587",{id:"formSmash:items:resultList:18:j_idt587",widgetVar:"widget_formSmash_items_resultList_18_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Villani, MattiasKohn, RobertPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:18:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Speeding up MCMC by efficient data subsamplingManuscript (preprint) (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_18_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:18:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_18_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for data sets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of the data, resulting in substantially fewer density evaluations. The data subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion probability of an observation is proportional to an approximation of its contribution to the log-likelihood function. Three broad classes of approximations are presented. The proposed algorithm is shown to sample from a distribution that is within O(m

^{-1/2}) of the true posterior, where m is the subsample size. Moreover, the constant in the O(m^{-1/2}) error bound of the likelihood is shown to be small and the approximation error is demonstrated to be negligible even for a small m in our applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a fixed computational budget. The method is applied to a bivariate probit model on a data set with half a million observations, and on a Weibull regression model with random effects for discrete-time survival data.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:18:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 270. Riazoshams, Hossein PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt584",{id:"formSmash:items:resultList:19:j_idt584",widgetVar:"widget_formSmash_items_resultList_19_j_idt584",onLabel:"Riazoshams, Hossein ",offLabel:"Riazoshams, Hossein ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:19:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Robustifying the Least Squares estimate of parameters of variance model function in nonlinear regression with heteroscedastic variance2012Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_19_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:19:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_19_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The purpose of this research is to propose a robust estimate for the parameters of a nonlinear regression model and its residual variance model parameters, when the residuals follow a heteroscedastic parametric model function. The classic estimate is based on the least squares estimation error for the model parameters and the least square estimate error between sample variance and variance model, for the parameters of variance function model. The sample variance that are computed from the data set, are used as the initial estimates of variance model. In the presence of outliers these estimators are not Robust, and tends to infinity. Both function model parameter estimates and variance model parameter estimates must be robustified to solve the outlier effect problems. In this research the MM-estimator is applied for robust estimating the function model parameters and M-estimator is applied for robust estimating of variance function model parameters. These estimators finally combined and the Extended Generalized Estimator is calculated.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:19:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 271. Riazoshams, Hossein PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt584",{id:"formSmash:items:resultList:20:j_idt584",widgetVar:"widget_formSmash_items_resultList_20_j_idt584",onLabel:"Riazoshams, Hossein ",offLabel:"Riazoshams, Hossein ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt587",{id:"formSmash:items:resultList:20:j_idt587",widgetVar:"widget_formSmash_items_resultList_20_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Lamerd University.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Habshah, MidiUniversity Putra Malaysia.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:20:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Application of Robust Nonlinear Regression, case study for modeling the greenhouse gases, Methane and Carbon Dioxide concentration in atmosphere2013Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_20_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:20:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_20_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Four nonlinear regression models are proposed for the atmospheric carbon dioxide and methane gas concentrations data, reported by United Nation 1989. Within those, the Exponential with Intercept is the most preferred one due to better convergence and lower correlation between parameters. Due to large range of data that back to history to 7000 years ago, there is a big dispersion in data set, so that it made us to apply robust nonlinear regression estimation methods to have a smoother model

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:20:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 272. Riazoshams, Hossein PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt584",{id:"formSmash:items:resultList:21:j_idt584",widgetVar:"widget_formSmash_items_resultList_21_j_idt584",onLabel:"Riazoshams, Hossein ",offLabel:"Riazoshams, Hossein ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt587",{id:"formSmash:items:resultList:21:j_idt587",widgetVar:"widget_formSmash_items_resultList_21_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Islamic Azad University, Iran.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Midi, Habshah Bt.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:21:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The Performance of a Robust Multistage Estimator in Nonlinear Regression with Heteroscedastic Errors2016In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 9, p. 3394-3415Article, review/survey (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_21_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:21:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_21_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this article, a robust multistage parameter estimator is proposed for nonlinear regression with heteroscedastic variance, where the residual variances are considered as a general parametric function of predictors. The motivation is based on considering the chi-square distribution for the calculated sample variance of the data. It is shown that outliers that are influential in nonlinear regression parameter estimates are not necessarily influential in calculating the sample variance. This matter persuades us, not only to robustify the estimate of the parameters of the models for both the regression function and the variance, but also to replace the sample variance of the data by a robust scale estimate.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:21:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 273. Riazoshams, Hossein et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt587",{id:"formSmash:items:resultList:22:j_idt587",widgetVar:"widget_formSmash_items_resultList_22_j_idt587",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:22:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Midi, HabshahGhilagaber, GebrenegusStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:22:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Robust Nonlinear Regression: With Application Using R2018Book (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_22_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:22:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_22_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); *Robust Nonlinear Regression: with Applications using R*develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language, under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by both practitioners and researchers.The main areas that will be covered in the book include theories and application of Nonlinear Robust Regression. In both parts the classic and robust aspects of nonlinear regression will be discussed. Outlier effects is among the main focus in the book.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:22:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 274. Rodgers, Joseph Lee et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_23_j_idt587",{id:"formSmash:items:resultList:23:j_idt587",widgetVar:"widget_formSmash_items_resultList_23_j_idt587",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:23:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Wänström, LindaStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:23:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Identification of a Flynn Effect in the NLSY: Moving from the center to the boundaries2007In: Intelligence, ISSN 0160-2896, Vol. 35, no 2, p. 187-196Article in journal (Refereed)275. Rosenblad, Andreas PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_24_j_idt584",{id:"formSmash:items:resultList:24:j_idt584",widgetVar:"widget_formSmash_items_resultList_24_j_idt584",onLabel:"Rosenblad, Andreas ",offLabel:"Rosenblad, Andreas ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:24:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); International Handbook of Research in Statistics Education by Dani Ben-Zvi, Katie Makar & Joan Garfield (Eds.), Springer, 20182019In: International Statistical Review, ISSN 0306-7734, E-ISSN 1751-5823, Vol. 87, no 1, p. 184-186Article, book review (Other academic)276. Sandström, Arne PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt584",{id:"formSmash:items:resultList:25:j_idt584",widgetVar:"widget_formSmash_items_resultList_25_j_idt584",onLabel:"Sandström, Arne ",offLabel:"Sandström, Arne ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:25:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimating income inequality: large sample inference in finite populations1983Doctoral thesis, monograph (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_25_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:25:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_25_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); During the last decades the interest in measuring income inequality has substantially increased. In this work we consider measures related to the Lorenz Curve, e g the Gini coefficient. These measures are decomposable^ g the inequality in disposable income can be assigned to various income sources. These components are ratios of linear functions of order and/or concomitant statistics. The asymptotic normality of these inequality measures is demonstrated. This theory makes it possible to make inference about the inequality in infinite populations. But the principal aim is to make inference about the finite population inequality. In a design approach it seems necessary to introduce assumptions on the asymptotic properties on the inclusion probabilities. To avoid this, we introduce a superpopulation model within which the inference of the finite population inequalities is supposed to be made. Within this model we have to consider the linear functions of order and/or concomitant statistics as functionals of weighted empirical distribution functions. The measures considered are shown to be asymptotically normal. In appendices the effect of tied observations is discussed.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:25:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 277. Sepp, Silvia et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_26_j_idt587",{id:"formSmash:items:resultList:26:j_idt587",widgetVar:"widget_formSmash_items_resultList_26_j_idt587",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:26:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.Möls, TõnuPaal, JaanusPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:26:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Study of the multivariate structure of the Estonian Alchemilla L. (Rosaceae) microspecies: an example of the structural indices approach.2000In: Proceedings of the Estonian Academy of Sciences. Biology, Ecology., ISSN 1406-0914, Vol. 49, no 3, p. 289 - 301Article in journal (Refereed)278. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt584",{id:"formSmash:items:resultList:27:j_idt584",widgetVar:"widget_formSmash_items_resultList_27_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:27:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Design-based estimators for snowball sampling2010Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_27_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:27:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_27_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Snowball sampling, where existing study subjects recruit further subjects from amongtheir acquaintances, is a popular approach when sampling from hidden populations.Since people with many in-links are more likely to be selected, there will be a selectionbias in the samples obtained. In order to eliminate this bias, the sample data must beweighted. However, the exact selection probabilities are unknown for snowball samplesand need to be approximated in an appropriate way. This paper proposes differentways of approximating the selection probabilities and develops weighting techniquesusing the inverse of the selection probabilities. Some numerical examples for smallgraphs and simulations on larger networks are provided to compare the efficiencyof the weighting techniques. The simulation results indicate that the suggested re-weighted estimators should be preferred to traditional estimators with equal sampleweights for the initial snowball sampling waves.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:27:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 279. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt584",{id:"formSmash:items:resultList:28:j_idt584",widgetVar:"widget_formSmash_items_resultList_28_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:28:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Length-Biased Sampling: A Review2007Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_28_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:28:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_28_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A frequent sampling design problem is length-biased sampling. This meansthat the probabilities of sample inclusion of population units are relatedto the values of the variable being measured. Parameter estimates maybecome biased and inconsistent if this sampling problem is ignored. Thispaper contains a review of applications characterized by length-biased samplingand the suggested solutions. The paper also includes a small simulationstudy on the properties of corrected mean estimators under misspecifiedsampling inclusion probabilities. Results indicate the importance ofcorrectly specified sampling inclusion mechanisms.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:28:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 280. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt584",{id:"formSmash:items:resultList:29:j_idt584",widgetVar:"widget_formSmash_items_resultList_29_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); On-Site Sampling in Economic Valuation Studies2007Licentiate thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_29_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:29:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_29_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A commonly used sampling design in economic valuation studies is on-sitesampling. If this sampling design is used, the sampling inclusion probabil-ities may be correlated with respondents’ valuations, invalidating welfaremeasures derived from estimates of the probit model. This problem is re-ferred to a length-bias, a problem discovered in other fields of applicationof statistics.The first paper in this thesis outlines different application fields thathave length-bias problems and the suggested model solutions in the litera-ture are presented.The second paper of this thesis proposes a model based on the bivariateordinal probit, a model that can be used to analyze binary choice CV datagathered by on-site sampling. The models is presented, the log-likelihoodis derived, and the properties of the MLE’s are illustrated using a smallsimulation study. The simulation results show the proposed estimator tobe an interesting alternative.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:29:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 281. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt584",{id:"formSmash:items:resultList:30:j_idt584",widgetVar:"widget_formSmash_items_resultList_30_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Random Multigraphs: Complexity Measures, Probability Models and Statistical Inference2012Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_30_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:30:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_30_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This thesis is concerned with multigraphs and their complexity which is defined and quantified by the distribution of edge multiplicities. Two random multigraph models are considered. The first model is random stub matching (RSM) where the edges are formed by randomly coupling pairs of stubs according to a fixed stub multiplicity sequence. The second model is obtained by independent edge assignments (IEA) according to a common probability distribution over the edge sites. Two different methods for obtaining an approximate IEA model from an RSM model are also presented.

In Paper I, multigraphs are analyzed with respect to structure and complexity by using entropy and joint information. The main results include formulae for numbers of graphs of different kinds and their complexity. The local and global structure of multigraphs under RSM are analyzed in Paper II. The distribution of multigraphs under RSM is shown to depend on a single complexity statistic. The distributions under RSM and IEA are used for calculations of moments and entropies, and for comparisons by information divergence. The main results include new formulae for local edge probabilities and probability approximation for simplicity of an RSM multigraph. In Paper III, statistical tests of a simple or composite IEA hypothesis are performed using goodness-of-fit measures. The results indicate that even for very small number of edges, the null distributions of the test statistics under IEA have distributions that are well approximated by their asymptotic χ

^{2}-distributions. Paper IV contains the multigraph algorithms that are used for numerical calculations in Papers I-III.PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:30:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 282. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_31_j_idt584",{id:"formSmash:items:resultList:31:j_idt584",widgetVar:"widget_formSmash_items_resultList_31_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:31:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Random Stub Matching Models of MultigraphsManuscript (preprint) (Other academic)283. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_32_j_idt584",{id:"formSmash:items:resultList:32:j_idt584",widgetVar:"widget_formSmash_items_resultList_32_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Some Multigraph AlgorithmsManuscript (preprint) (Other academic)284. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_33_j_idt584",{id:"formSmash:items:resultList:33:j_idt584",widgetVar:"widget_formSmash_items_resultList_33_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:33:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Statistical Analysis of MultigraphsManuscript (preprint) (Other academic)285. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt584",{id:"formSmash:items:resultList:34:j_idt584",widgetVar:"widget_formSmash_items_resultList_34_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt587",{id:"formSmash:items:resultList:34:j_idt587",widgetVar:"widget_formSmash_items_resultList_34_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Frank, OveStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:34:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Complexity of Families of Multigraphs2012In: 2012 JSM Proceedings: Papers Presented at the Joint Statistical Meetings, San Diego, California, July 28-August 2, 2012, and Other ASA-sponsored Conferences, American Statistical Association , 2012Conference paper (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_34_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:34:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_34_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This article describes families of finite multigraphs with labeled or unlabeled edges and vertices. It shows how size and complexity vary for different types of equivalence classes of graphs defined by ignoring only edge labels or ignoring both edge and vertex labels. Complexity is quantified by the distribution of edge multiplicities, and different complexity measures are discussed. Basic occupancy models for multigraphs are used to illustrate different graph distributions on isomorphism and complexity. The loss of information caused by ignoring edge and vertex labels is quantified by entropy and joint information that provide tools for studying properties of and relations between different graph families.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:34:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 286. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt584",{id:"formSmash:items:resultList:35:j_idt584",widgetVar:"widget_formSmash_items_resultList_35_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt587",{id:"formSmash:items:resultList:35:j_idt587",widgetVar:"widget_formSmash_items_resultList_35_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); University of Konstanz, Germany.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Frank, OveStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:35:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation of global network properties by using local aggregated data.2014Conference paper (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_35_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:35:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_35_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Estimation of global network properties by using local aggregated data. We consider networks that can be modeled as random multigraphs and indicate various applications and appearances of multigraphs. Global properties are measured by entropy and complexity based on the edge multiplicities. Sample data give partial information about the network, and we consider a special kind of local information given by aggregation of vertices and counting of edges within and between blocks of vertices. Shafie (2012) gives a comprehensive analysis and comparison of random multigraphs of different kinds, and Frank and Shafie (2013) present special results about complexity and data aggregation in multigraphs.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:35:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 287. Shafie, Termeh et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt587",{id:"formSmash:items:resultList:36:j_idt587",widgetVar:"widget_formSmash_items_resultList_36_j_idt587",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}); Frank, OveStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:36:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Evaluating network centrality using entropy tools2016In: XXXVI International Sunbelt Social Network Conference: Presentation and Poster Abstract, 2016, p. 195-196Conference paper (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_36_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:36:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_36_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); We recently introduced a new way of using statistical entropies to capture interdependencies among vertex and edge variables in multivariate networks. These entropies are used to systematically check for tendencies in the multidimensional variable set concerning redundancies, functional relationships, independencies and conditional independencies among different variable combinations. An important use of this technique is to apply it for selection of good summary measures of network structure. For instance, there are many alternative network statistics available for measuring centrality, and it is not always easy to decide which one is appropriate for a current application. In this presentation, we consider different centrality statistics among the variables in the analysis. By using univariate and multivariate entropies, we aim to find the centrality measure that is most relevant for the network property of interest. Throughout this presentation, we use John Padgett’s extended Florentine network data consisting of 87 families where the vertices as well as the edges have numerical or qualitative attributes defined on them. We create edge and vertex variables that capture network information via the most common centrality measures. The dependence structure of the variables is then explored by entropy analysis and it is determined which centrality measure is most appropriate for representing political, social or economic influence among the Florentine families. Further, we demonstrate how divergence measures can be used to indicate and test structural tendencies with respect to centrality in this network.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:36:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 288. Shafie, Termeh et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt587",{id:"formSmash:items:resultList:37:j_idt587",widgetVar:"widget_formSmash_items_resultList_37_j_idt587",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:37:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Frank, OveStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:37:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Finding informative triads2017Conference paper (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_37_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:37:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_37_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Network data containing attributes of both vertices and vertex pairs can be analysed together using multivariate entropies. This general multivariate technique is here illustrated with data previously analysed by Lazega and others. We focus here on the role of triad count statistics for exploring the dependency structure of the network attributes.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:37:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 289. Shafie, Termeh PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt584",{id:"formSmash:items:resultList:38:j_idt584",widgetVar:"widget_formSmash_items_resultList_38_j_idt584",onLabel:"Shafie, Termeh ",offLabel:"Shafie, Termeh ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt587",{id:"formSmash:items:resultList:38:j_idt587",widgetVar:"widget_formSmash_items_resultList_38_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Laitila, ThomasÖrebro Universitet.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:38:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); A Binary Ordinal Probit Model for CVM Data Based on On-Site Samples2007Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_38_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:38:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_38_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); A commonly used sampling design in economic valuation studies such as theCVM, is on-site sampling. If this sampling design is used, the sampling inclu-sion probabilities may be correlated with respondents’ valuations, invalidatingwelfare measures derived from estimates of the probit model. This paper pro-poses a model based on the bivariate ordinal probit, a model that can be usedto analyze binary choice CV data gathered by on-site sampling. This paperpresents the model, derives the log-likelihood, and illustrates the MLE using asmall simulation study. The model is presented, the log-likelihood is derived andthe properties of the MLE’s are illustrated with a small simulation study. Thesimulation results show the proposed estimator to be an interesting alternative,but the estimator should be evaluated in more population models.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:38:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 290. Shahnazarian, Hovick et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt587",{id:"formSmash:items:resultList:39:j_idt587",widgetVar:"widget_formSmash_items_resultList_39_j_idt587",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:39:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Solberger, MartinSpånberg, ErikStockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:39:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Forecasting and Analysing Corporate Tax Revenues in Sweden Using Bayesian VAR Models2017In: Finnish economic papers, ISSN 0784-5197, Vol. 28, no 1, p. 50-74Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_39_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:39:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_39_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Corporate tax revenue forecasts are important for governmental agencies, but are complicated to achieve with high precision and generally also difficult to connect to governments’ macroeconomic forecasts. This paper proposes a solution to these problems by decomposing corporate tax revenues and connecting the components to different determinants using Bayesian VAR models. Applied to Sweden, we find that most of the variation in forecasting errors of net operating surplus and net business income are attributable to shocks in factors identified in the literature, and that the forecasting performance is improved by conditioning on the macroeconomic development.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:39:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 291. Sjöström, Olle PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_40_j_idt584",{id:"formSmash:items:resultList:40:j_idt584",widgetVar:"widget_formSmash_items_resultList_40_j_idt584",onLabel:"Sjöström, Olle ",offLabel:"Sjöström, Olle ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.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}); Svensk samhällsstatistik: etik, policy och planering : några synsätt på samhällsstatistikens problem och funktion, skiss av ett forskningsprogram, förslag till utveckling av den statistiska redovisningen samt exemplifieringar1980Doctoral thesis, monograph (Other academic)292. Spånberg, Erik PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt584",{id:"formSmash:items:resultList:41:j_idt584",widgetVar:"widget_formSmash_items_resultList_41_j_idt584",onLabel:"Spånberg, Erik ",offLabel:"Spånberg, Erik ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt587",{id:"formSmash:items:resultList:41:j_idt587",widgetVar:"widget_formSmash_items_resultList_41_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics. Ministry of Finance, Sweden.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Shahnazarian, HovickPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:41:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); The importance of the financial system for the current account in Sweden: A sectoral approach2019In: International Economics, ISSN 2110-7017, Vol. 158, p. 91-103Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_41_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:41:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_41_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper takes a sectoral approach to investigate the importance of financial variables for current account dynamics in Sweden. We use a Bayesian VAR model with priors on the steady states to analyse the determinants of net lending in different sectors and by extension the current account. The results suggest that: (i) the sectoral approach provides added value in analysing and understanding the drivers of the current account; (ii) demographic and financial shocks each account for 10–40 per cent of the forecast error variance of net lending in different sectors; (iii) negative development in the financial variables has a negative impact on the current account; and (iv) the importance and impact magnitudes of financial shocks depend on how economic policies are assumed to counteract these shocks.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:41:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 293. Srivastava, Muni S. et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt587",{id:"formSmash:items:resultList:42:j_idt587",widgetVar:"widget_formSmash_items_resultList_42_j_idt587",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:42:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:42:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation in General Multivariate Linear Models with Kronecker Product Covariance Structure2008Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_42_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:42:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_42_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this article models based on pq-dimensional normally distributed random vectors x are studied with a mean vec(ABC), where A and C

are known matrices, and a separable covariance matrix $\psi\otimes \Sigma$, where both $\Psi$ and $\Sigma$ are positive definite and except the estimability condition $\psi_{qq} = 1$, unknown. The model may among others be applied when spatial-temporal relationships exist. On the basis of n independent observations on the random vector x, we wish to estimate the parameters of the model. In the paper estimation equations for obtaining maximum likelihood estimators are presented. It is shown that there exist only one solution to these equations. Likelihood equations are also considered when $FBG = 0$, with F and G known. Moreover, the likelihood ratio test for testing $FBG = 0$ against $FBG\neq = 0$ is considered.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:42:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 294. Srivastava, Muni S. et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_43_j_idt587",{id:"formSmash:items:resultList:43:j_idt587",widgetVar:"widget_formSmash_items_resultList_43_j_idt587",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:43:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:43:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Models with a Kronecker Product Covariance Structure: Estimation and Testing2007In: Research Report, Centre of Biostochastics, Swedish University of Agricultural Sciences, ISSN 1651-8543, Vol. 7, p. 1-28Article in journal (Refereed)295. Srivastava, Muni S. et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt587",{id:"formSmash:items:resultList:44:j_idt587",widgetVar:"widget_formSmash_items_resultList_44_j_idt587",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:44:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Nahtman, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:44:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Models with a Kronecker Product Covariance Structure: Estimation and Testing2007Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_44_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:44:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_44_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); In this article we consider a $pq$-dimensional random vector $x$ distributed normally with mean vector $\theta$ and the covariance matrix $\Lambda$, assumed to be positive definite. On the basis of $N$ independent observations on the random vector $x$, we wish to estimate parameters and test the hypothesis $H: \Lambda=\Psi\otimes\Sigma$, where $\Psi = (\psi_{ij}) : q\times q$ and $\Sigma = (\sigma_{ij}) : p\times p$, and $\Lambda =

(\psi_{ij}\Sigma)$, the Kronecker product of $\Psi$ and $\Sigma$. That is instead of $\frac{1}{2}pq(pq+1)$ parameters, it has only $\frac{1}{2}p(p + 1) + \frac{1}{2}q(q + 1) - 1$ parameters. When this model holds, we test the hypothesis that $\Psi$ is an identity matrix, a diagonal matrix or of intraclass correlation structure. The maximum likelihood estimators (MLE) are obtained under the hypothesis as well as under the alternatives. Using these estimators the likelihood ratio tests (LRT) are obtained. Moreover, it is shown that the estimators are unique.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:44:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 296. Srivastava, Muni S. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt584",{id:"formSmash:items:resultList:45:j_idt584",widgetVar:"widget_formSmash_items_resultList_45_j_idt584",onLabel:"Srivastava, Muni S. ",offLabel:"Srivastava, Muni S. ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_45_j_idt587",{id:"formSmash:items:resultList:45:j_idt587",widgetVar:"widget_formSmash_items_resultList_45_j_idt587",onLabel:"et al.",offLabel:"et al.",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Department of Statistics, University of Toronto, Canada.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.von Rosen, DietrichInstitutionen för energi och teknik, SLU.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:45:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Estimation and testing in general multivariate linear models with Kronecker product covariance structure2009In: Sankhya - The Indian Journal of Statistics, ISSN 0972-7671, E-ISSN 0976-3139, Vol. 71-A, no 2, p. 137-163Article in journal (Refereed)297. Srivastava, Muni S. et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_46_j_idt587",{id:"formSmash:items:resultList:46:j_idt587",widgetVar:"widget_formSmash_items_resultList_46_j_idt587",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:46:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); von Rosen, TatjanaStockholm University, Faculty of Social Sciences, Department of Statistics.von Rosen, DietrichPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:46:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Models with a Kronecker Product Covariance Structure: Estimation and Testing2008In: Mathematical Methods of Statistics, ISSN 1066-5307, Vol. 17, no 4, p. 357-370Article in journal (Refereed)298. Stallard, Nigel et al. PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt587",{id:"formSmash:items:resultList:47:j_idt587",widgetVar:"widget_formSmash_items_resultList_47_j_idt587",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:47:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Miller, FrankStockholm University, Faculty of Social Sciences, Department of Statistics.Day, SimonHee, Siew WanMadan, JasonZohar, SarahPosch, MartinPrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:47:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Determination of the optimal sample size for a clinical trial accounting for the population size2017In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 59, no 4, p. 609-625Article in journal (Refereed)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_47_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:47:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_47_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflectthe size of the population under consideration. Incorporation of the population size is possible in adecision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N^1/2) or O(N∗^1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:47:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 299. Stockhammar, Pär PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt584",{id:"formSmash:items:resultList:48:j_idt584",widgetVar:"widget_formSmash_items_resultList_48_j_idt584",onLabel:"Stockhammar, Pär ",offLabel:"Stockhammar, Pär ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:48:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Comovements of the Dow Jones Stock Index and US GDP2010Report (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_48_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:48:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_48_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); This paper explores the connection between Dow Jones industrial average (DJIA) stock prices and the US GDP growth. Both series are heteroscedastic, making standard detrending procedures, such as Hodrick-Prescott or Baxter-King, inadequate. The results from these procedures are compared to the results from heteroscedasticity corrected data, thus the effect of the neglected heteroscedasticity is measured. The analysis is mainly done in the frequency domain but relevant time domain results are also reported.

PrimeFaces.cw("Panel","tryPanel",{id:"formSmash:items:resultList:48:j_idt622:0:abstractPanel",widgetVar:"tryPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); 300. Stockhammar, Pär PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt584",{id:"formSmash:items:resultList:49:j_idt584",widgetVar:"widget_formSmash_items_resultList_49_j_idt584",onLabel:"Stockhammar, Pär ",offLabel:"Stockhammar, Pär ",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Stockholm University, Faculty of Social Sciences, Department of Statistics.PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:orgPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); PrimeFaces.cw("Panel","testPanel",{id:"formSmash:items:resultList:49:etAlPanel",widgetVar:"testPanel",toggleable:true,toggleSpeed:500,collapsed:false,toggleOrientation:"vertical",closable:true,closeSpeed:500}); Some Contributions to Filtering, Modeling and Forecasting of Heteroscedastic Time Series2010Doctoral thesis, comprehensive summary (Other academic)Abstract [en] PrimeFaces.cw("SelectBooleanButton","widget_formSmash_items_resultList_49_j_idt622_0_j_idt623",{id:"formSmash:items:resultList:49:j_idt622:0:j_idt623",widgetVar:"widget_formSmash_items_resultList_49_j_idt622_0_j_idt623",onLabel:"Abstract [en]",offLabel:"Abstract [en]",onIcon:"ui-icon-triangle-1-s",offIcon:"ui-icon-triangle-1-e"}); Heteroscedasticity (or time-dependent volatility) in economic and financial time series has been recognized for decades. Still, heteroscedasticity is surprisingly often neglected by practitioners and researchers. This may lead to inefficient procedures. Much of the work in this thesis is about finding more effective ways to deal with heteroscedasticity in economic and financial data. Paper I suggest a filter that, unlike the Box-Cox transformation, does not assume that the heteroscedasticity is a power of the expected level of the series. This is achieved by dividing the time series by a moving average of its standard deviations smoothed by a Hodrick-Prescott filter. It is shown that the filter does not colour white noise.

An appropriate removal of heteroscedasticity allows more effective analyses of heteroscedastic time series. A few examples are presented in Paper II, III and IV of this thesis. Removing the heteroscedasticity using the proposed filter enables efficient estimation of the underlying probability distribution of economic growth. It is shown that the mixed Normal - Asymmetric Laplace (NAL) distributional fit is superior to the alternatives. This distribution represents a Schumpeterian model of growth, the driving mechanism of which is Poisson (Aghion and Howitt, 1992) distributed innovations. This distribution is flexible and has not been used before in this context. Another way of circumventing strong heteroscedasticity in the Dow Jones stock index is to divide the data into volatility groups using the procedure described in Paper III. For each such group, the most accurate probability distribution is searched for and is used in density forecasting. Interestingly, the NAL distribution fits best also here. This could hint at a new analogy between the financial sphere and the real economy, further investigated in Paper IV. These series are typically heteroscedastic, making standard detrending procedures, such as Hodrick-Prescott or Baxter-King, inadequate. Prior to this comovement study, the univariate and bivariate frequency domain results from these filters are compared to the filter proposed in Paper I. The effect of often neglected heteroscedasticity may thus be studied.

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