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  • 1.
    Andersson, Marta
    et al.
    Stockholm University, Faculty of Humanities, Department of English.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Subjectivity (Re)visited: A Corpus Study of English Forward Causal Connectives in Different Domains of Spoken and Written Language2021In: Discourse processes, ISSN 0163-853X, E-ISSN 1532-6950, Vol. 58, no 3, p. 260-292Article in journal (Refereed)
    Abstract [en]

    Through a structured examination of four English causal discourse connectives, our article tackles a gap in the existing research, which focuses mainly on written language production, and entirely lacks attests on English spoken discourse. Given the alleged general nature of English connectives commonly emphasized in the literature, the underlying question of our investigation is the potential role of the connective phrases in marking the basic conceptual distinction between objective and subjective causal event types. To this end, our study combines a traditional corpus analysis with 'predictive' statistical modeling for subjectivity variables to investigate whether and how the tendencies found in the corpus depend on the systematic preferences of the language user to encode subjectivity via a discourse connective. Our findings suggest that while certain conceptual structures are quite fundamental to the usages of English connectives, the connectives per se do not seem to have a steady part in categorization of causal events. Rather, their role pertains to the level of intended explicitness bound to specific rhetorical purposes and contexts of use.

    Download full text (pdf)
    fulltext
  • 2.
    Björkström, Anders
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    A two-parametric class of predictors in multivariate regression2007In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 21, no 5-6, p. 215-226Article in journal (Refereed)
    Abstract [en]

    We demonstrate that a number of well-established multivariate regression methods for prediction are related in that they are special cases of basically one general procedure. We try a more general method based on this procedure with two metaparameters. In a simulation study, based on a latent structure model, we compare this method to ridge regression (RR), multivariate partial least squares regression (PLSR) and repeated univariate PLSR. For most types of data sets studied, all methods do approximately equally well. There are some cases where RR and least squares ridge regression (LSRR) yield larger errors than the other methods, and we conclude that one-factor methods are not adequate for situations where more than one latent variable are needed to describe the data. Among those based on latent variables, none of the methods tried is superior to the others in any obvious way.

  • 3.
    Björkström, Anders
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Continuum regression is not always continuous1996In: Journal of The Royal Statistical Society Series B-statistical Methodology, ISSN 1369-7412, E-ISSN 1467-9868, Vol. B 58, no 4, p. 703-710Article in journal (Refereed)
  • 4.
    Bojarova, Jelena
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Gustafsson, Nils
    Aspects of non-linearities for data assimilation by Kalman filtering in a shallow water modelManuscript (preprint) (Other academic)
  • 5.
    Bojarova, Jelena
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Decomposition of time series of geological data into long and short timescale variation under non-Gaussian state space models2008Report (Other academic)
  • 6. Bojarova, Jelena
    et al.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Decomposition of time series of geological data into long- and short-timescale variations under non-Gaussian state space models2009Manuscript (preprint) (Other academic)
  • 7.
    Bojarova, Jelena
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Non-Gaussian state space models in decomposition of ice core time series in long and short time-scales2010In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 21, no 6, p. 562-587Article in journal (Refereed)
    Abstract [en]

    Statistical modelling of six time series of geological ice core chemical data from Greenland is discussed. We decompose the total variation into long time-scale (trend) and short time-scale variations (fluctuations around the trend), and a pure noise component. Too heavy tails of the short-term variation makes a standard time-invariant linear Gaussian model inadequate. We try non-Gaussian state space models, which can be efficiently approximated by time-dependent Gaussian models. In essence, these time-dependent Gaussian models result in a local smoothing, in contrast to the global smoothing provided by the time-invariant model. To describe the mechanism of this local smoothing, we utilise the concept of a local variance function derived from a heavy-tailed density. The time-dependent error variance expresses the uncertainty about the dynamical development of the model state, and it controls the influence of observations on the estimates of the model state components. The great advantage of the derived time-dependent Gaussian model is that the Kalman filter and the Kalman smoother can be used as efficient computational tools for performing the variation decomposition. One of the main objectives of the study is to investigate how the distributional assumption on the model error component of the short time-scale variation affects the decomposition.

  • 8.
    Castensson, Anja
    et al.
    Uppsala University, Sweden.
    Emilsson, Lina
    Uppsala University, Sweden.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Jazin, Elena
    Uppsala University, Sweden.
    Decrease of serotonin receptor 2C in schizophrenia brains identified by high-resolution mRNA expression analysis2003In: Biological Psychiatry, ISSN 0006-3223, E-ISSN 1873-2402, Vol. 54, no 11, p. 1212-1221Article in journal (Refereed)
    Abstract [en]

    Background: RNA expression profiling can provide hints for the selection of candidate susceptibility genes, for formulation of hypotheses about the development of a disease, and/or for selection of candidate gene targets for novel drug development. We measured messenger RNA expression levels of 16 candidate genes in brain samples from 55 schizophrenia patients and 55 controls. This is the largest sample so far used to identify genes differentially expressed in schizophrenia brains.

    Methods: We used a sensitive real-time polymerase chain reaction methodology and a novel statistical approach, including the development of a linear model of analysis of covariance type.

    Results: We found two genes differentially expressed: monoamine oxidase B was significantly increased in schizophrenia brain (p = .001), whereas one of the serotonin receptor genes, serotonin receptor 2C, was significantly decreased (p = .001). Other genes, previously proposed to be differentially expressed in schizophrenia brain, were invariant in our analysis.

    Conclusions: The differential expression of serotonin receptor 2C is particularly relevant for the development of new atypical antipsychotic drugs. The strategy presented here is useful to evaluate hypothesizes for the development of the disease proposed by other investigators.

  • 9.
    Fetisova, Ekaterina
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Brattström, Gudrun
    Stockholm University, Faculty of Science, Department of Mathematics.
    Moberg, Anders
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings: Part IManuscript (preprint) (Other academic)
    Abstract [en]

    Evaluation of climate model simulations is a crucial task in climate research. In a work consisting of three parts, we propose a new statistical framework for evaluation of simulated responses to climate forcings, based on the concept of latent (unobservable) factors. Here, in Part I, we suggest several latent factor models of different complexity that can be used for evaluation of temperature data from climate model simulations against climate proxy data from the last millennium. Each factor model is developed for use with data from a single region, which can be of any size. To be able to test the hypotheses of interest, we have applied the technique of confirmatory factor analysis. We also elucidate the link between our factor models and the statistical methods used in Detection and Attribution (D\&A) studies. In particular, we demonstrate that our factor models can be used as an alternative approach to the methods used in D\&A studies. An additional advantage of their use is that they, in contrast to the commonly used D\&A methods, make it, in principle, possible to investigate whether the forcings of interest act additively or if any interaction effects exist.In Part II we investigate and illustrate the expansion of factor models to structural equation models, which permits the statistical modelling of more complicated climatological relationships. The performance of some of our statistical models suggested in Part I and Part is evaluated and compared in a numerical experiment, whose results are presented in Part III.

    Download full text (pdf)
    fulltext
  • 10.
    Gummesson, Sara
    et al.
    Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Osteoarchaeological Research Laboratory.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Knutsson, Helena
    Zetterlund, Peter
    Molin, Fredrik
    Knutsson, Kjel
    Lithic Raw Material Economy in the Mesolithic: Experimental Test of Edged Tool Efficiency and Durability in Bone Tool Production2017In: Lithic Technology, ISSN 0197-7261, Vol. 42, no 4, p. 140-154Article in journal (Refereed)
    Abstract [en]

    The foundation of this paper is lithic economy with a focus on the actual use of different lithic raw materials for tasks at hand. Our specific focus is on the production of bone tools during the Mesolithic. The lithic and osseous assemblages from Strandvägen, Motala, in east-central Sweden provide the archaeological background for the study. Based on a series of experiments we evaluate the efficiency and durability of different tool edges of five lithic raw materials: Cambrian flint, Cretaceous flint, mylonitic quartz, quartz, and porphyry, each used to whittle bone. The results show that flint is the most efficient of the raw materials assessed. Thus, a non-local raw material offers complements of functional characteristics for bone working compared to locally available quartz and mylonitic quartz. This finding provides a new insight into lithic raw material distribution in the region, specifically for bone tool production on site. 

  • 11. Henningsson, Marcus
    et al.
    Östergren, Karin
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Dejmek, Petr
    Sensor fusion a a tool to monitor dynamic dairy processes2006In: Journal of Food Engineering, ISSN 0260-8774, E-ISSN 1873-5770, Vol. 76, no 2, p. 154-162Article in journal (Refereed)
    Abstract [en]

    A system for monitoring milk and fat concentration in a dynamic milk/water system by fusing information from several sensors was investigated. Standard instrumentation for food production was used, the sensors were a conductivity meter, a density meter and an optical instrument used to measure backscattered light. The system was applied to a dynamic mixing situation. Prediction error did not exceed 2% in the milk concentration and 0.1% fat in the total fat concentration. The applicability of the sensor fusion approach in field conditions was demonstrated by mounting the sensors in a dairy plant and monitoring the start-up of a pasteurizer. 

    Download full text (pdf)
    Henningsson et al sensor fusion.pdf
  • 12.
    Hind, Alistair
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Moberg, Anders
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 2: A pseudo-proxy study addressing the amplitude of solar forcing2012In: Climate of the Past, ISSN 1814-9324, E-ISSN 1814-9332, Vol. 8, no 4, p. 1355-1365Article in journal (Refereed)
    Abstract [en]

    The statistical framework of Part 1 (Sundberg et al., 2012), for comparing ensemble simulation surface temperature output with temperature proxy and instrumental records, is implemented in a pseudo-proxy experiment. A set of previously published millennial forced simulations (Max Planck Institute – COSMOS), including both "low" and "high" solar radiative forcing histories together with other important forcings, was used to define "true" target temperatures as well as pseudo-proxy and pseudo-instrumental series. In a global land-only experiment, using annual mean temperatures at a 30-yr time resolution with realistic proxy noise levels, it was found that the low and high solar full-forcing simulations could be distinguished. In an additional experiment, where pseudo-proxies were created to reflect a current set of proxy locations and noise levels, the low and high solar forcing simulations could only be distinguished when the latter served as targets. To improve detectability of the low solar simulations, increasing the signal-to-noise ratio in local temperature proxies was more efficient than increasing the spatial coverage of the proxy network. The experiences gained here will be of guidance when these methods are applied to real proxy and instrumental data, for example when the aim is to distinguish which of the alternative solar forcing histories is most compatible with the observed/reconstructed climate.

  • 13.
    Hultin, Emilie
    et al.
    KTH Biotechnology.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics. Matematisk statistik.
    Random loss of genetic segments during skin differentiation indicated by analysis of single cells2007In: Genetic Sequence Analysis by Microarray Technology, KTH , 2007Chapter in book (Other academic)
  • 14.
    Lashgari, Katarina
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics. Stockholm University, Faculty of Science, The Bolin Centre for Climate Research (together with KTH & SMHI).
    Brattström, Gudrun
    Stockholm University, Faculty of Science, Department of Mathematics. Stockholm University, Faculty of Science, The Bolin Centre for Climate Research (together with KTH & SMHI).
    Moberg, Anders
    Stockholm University, Faculty of Science, Department of Physical Geography. Stockholm University, Faculty of Science, The Bolin Centre for Climate Research (together with KTH & SMHI).
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics. Stockholm University, Faculty of Science, The Bolin Centre for Climate Research (together with KTH & SMHI).
    Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory2022In: Advances in Statistical Climatology, Meteorology and Oceanography, E-ISSN 2364-3587, Vol. 8, no 2, p. 225-248Article in journal (Refereed)
    Abstract [en]

    Evaluation of climate model simulations is a crucial task in climate research. Here, a new statistical framework is proposed for evaluation of simulated temperature responses to climate forcings against temperature reconstructions derived from climate proxy data for the last millennium. The framework includes two types of statistical models, each of which is based on the concept of latent (unobservable) variables: confirmatory factor analysis (CFA) models and structural equation modelling (SEM) models. Each statistical model presented is developed for use with data from a single region, which can be of any size. The ideas behind the framework arose partly from a statistical model used in many detection and attribution (D&A) studies. Focusing on climatological characteristics of five specific forcings of natural and anthropogenic origin, the present work theoretically motivates an extension of the statistical model used in D&A studies to CFA and SEM models, which allow, for example, for non-climatic noise in observational data without assuming the additivity of the forcing effects. The application of the ideas of CFA is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. The result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.

  • 15.
    Moberg, Anders
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Grudd, Håkan
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Hind, Alistair
    Stockholm University, Faculty of Science, Department of Physical Geography. Stockholm University, Faculty of Science, Department of Mathematics.
    Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 3: Practical considerations, relaxed assumptions, and using tree-ring data to address the amplitude of solar forcing2015In: Climate of the Past, ISSN 1814-9324, E-ISSN 1814-9332, Vol. 11, no 3, p. 425-448Article in journal (Refereed)
    Abstract [en]

    A statistical framework for evaluation of climate model simulations by comparison with climate observations from instrumental and proxy data (part 1 in this series) is improved by the relaxation of two assumptions. This allows autocorrelation in the statistical model for simulated internal climate variability and enables direct comparison of two alternative forced simulations to test whether one fits the observations significantly better than the other. The extended framework is applied to a set of simulations driven with forcings for the pre-industrial period 1000-1849 CE and 15 tree-ring-based temperature proxy series. Simulations run with only one external forcing (land use, volcanic, small-amplitude solar, or large-amplitude solar) do not significantly capture the variability in the tree-ring data - although the simulation with volcanic forcing does so for some experiment settings. When all forcings are combined (using either the small- or large-amplitude solar forcing), including also orbital, greenhouse-gas and non-volcanic aerosol forcing, and additionally used to produce small simulation ensembles starting from slightly different initial ocean conditions, the resulting simulations are highly capable of capturing some observed variability. Nevertheless, for some choices in the experiment design, they are not significantly closer to the observations than when unforced simulations are used, due to highly variable results between regions. It is also not possible to tell whether the small-amplitude or large-amplitude solar forcing causes the multiple-forcing simulations to be closer to the reconstructed temperature variability. Proxy data from more regions and of more types, or representing larger regions and complementary seasons, are apparently needed for more conclusive results from model-data comparisons in the last millennium.

  • 16.
    Norén, G. Niklas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Bate, Andrew
    Edwards, Ralph
    A statistical methodology for drug–drug interaction surveillance2008In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 16, p. 3057-3070Article in journal (Refereed)
    Abstract [en]

    Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

  • 17.
    Persson, Jan-Olov
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Multigene analysis can discriminate between ulcerative colitis, Crohn's disease and irritable bowel syndorme2007Report (Other academic)
  • 18. Rydén, Jesper
    et al.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Willy Feller vid Stockholms högskola, 1934–1939: En gigant inom sannolikhetsteorin på svensk mark2021In: Qvintense, ISSN 2000-1819, Vol. 2021, no 2, p. 4-8Article in journal (Other academic)
    Abstract [en]

    Harald Cramér utnämndes 1929 till professor i forsäkringsmatematik och matematisk statistik vid Stockholms högskola. Efter nagra år hade han ett eget institut, Institutet for forsäkringsmatematik och matematisk statistik, med egna lokaler vid Odengatan, och 1-2 anstallda amanuenser. Där bedrevs en livlig verksamhet.

    Cramér hade ett brett kontakt nät bland stora namn inom sannolikhetsteorin. I hans outgivna memoarer, "Korta minnen fran ett långt liv", kan man läsa om nära kontakter med t.ex. Lévy och Féechet i Paris. I Institutets seminarieserie märks internationella namn som t.ex. Theodore (Ted) W. Anderson, Jerzy Neyman - och från 1934 en begåvad ung matematiker vid namn Willy Feller, som gästade Institutet under nära fem års tid.

    Det är Feller som är ämnet för var uppsats, en sedermera berömd probabilist, inte minst känd för sina två böcker i ämnet. 

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    Feller
  • 19.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    A classical dataset from Williams, and its role in the study of supersaturated designs.2008In: Journal of Chemometrics, Vol. 22, p. 436-440Article in journal (Refereed)
    Abstract [en]

    A Plackett–Burman type dataset from a paper by Williams (1968), with 28 observations and 24 two-level factors, has become a standard dataset for illustrating construction (by halving) of supersaturated designs (SSDs) and for a corresponding data analysis. The aim here is to point out that for several reasons this is an unfortunate situation. The original paper by Williams contains several errors and misprints. Some are in the design matrix, which will here be reconstructed, but worse is an outlier in the response values, which can be observed when data are plotted against the dominating factor. In addition, the data should better be analysed on log-scale than on original scale. The implications of the outlier for SSD analysis are drastic, and it will be concluded that the data should be used for this purpose only if the outlier is properly treated (omitted or modified).

  • 20.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    A Note on “Shaved Dice” Inference2018In: American Statistician, ISSN 0003-1305, E-ISSN 1537-2731, Vol. 72, no 2, p. 155-157Article in journal (Refereed)
    Abstract [en]

    Two dice are rolled repeatedly, only their sum is registered. Have the two dice been shaved, so two of the six sides appear more frequently? Pavlides and Perlman discussed this somewhat complicated type of situation through curved exponential families. Here, we contrast their approach by regarding data as incomplete data from a simple exponential family. The latter, supplementary approach is in some respects simpler, it provides additional insight about the relationships among the likelihood equation, the Fisher information, and the EM algorithm, and it illustrates the information content in ancillary statistics.

    Download full text (pdf)
    fulltext
  • 21.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Chemometrics2011In: International Encyclopedia of Statistical Science / [ed] Miodrag Lovric, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2011, p. 240-242Chapter in book (Refereed)
  • 22.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Collinearity2002In: Encyclopedia of Environmetrics / [ed] Abdel H. El-Shaarawi; Walter W. Piegorsch, Chichester: John Wiley & Sons, 2002, p. 365-366Chapter in book (Refereed)
  • 23.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Exponential Family Models2011In: International Encyclopedia of Statistical Science / [ed] Miodrag Lovric, Springer Berlin/Heidelberg, 2011, p. 490-493Chapter in book (Refereed)
  • 24.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Flat and multimodal likelihoods and model lack of fit in curved exponential families2010In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 37, no 4, p. 632-643Article in journal (Refereed)
    Abstract [en]

    It is well known that curved exponential families can have multimodal likelihoods. We investigate the relationship between flat or multimodal likelihoods and model lack of fit, the latter measured by the score (Rao) test statistic of the curved model as embedded in the corresponding full model. When data yield a locally flat or convex likelihood (root of multiplicity >1, terrace point, saddle point, local minimum), we provide a formula for in such points, or a lower bound for it. The formula is related to the statistical curvature of the model, and it depends on the amount of Fisher information. We use three models as examples, including the Behrens-Fisher model, to see how a flat likelihood, etc. by itself can indicate a bad fit of the model. The results are related (dual) to classical results by Efron from 1978.

  • 25.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Historik över avdelningen för matematisk statistik, SU: with English summary2013Other (Other (popular science, discussion, etc.))
    Download full text (pdf)
    Historikmatstat-2013.pdf
  • 26.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    'Institutets' historia under Stockholms högskolas tid: Sammanställningar av Rolf Sundberg, april 20132013Other (Other (popular science, discussion, etc.))
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    fulltext
  • 27.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Kan man lära till statistisk konsult på en kurs?2013In: Qvintense, ISSN 2000-1819, no 2, p. 7-9Article in journal (Other (popular science, discussion, etc.))
    Download full text (pdf)
    fulltext
  • 28.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Lineära Statistiska Modeller: Kompendium2021Other (Other academic)
  • 29.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Minnen och meningar2017In: Qvintense, ISSN 2000-1819, Vol. 2017, no 2, p. 20-22Article in journal (Other academic)
    Abstract [sv]

    Rolf Sundberg utsågs av Statistikfrämjandet till årets statistikfrämjare. Här berättar han om hur han blev statistiker och sedan fortsatt att jobba inom detta område. Dessutom synpunkter på kurser, forskning, teori/tillämpningar och bredd/djup i forskningen.

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    Minnen och meningar.pdf
  • 30.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Multicollinearity2002In: Encyclopedia of Environmetrics / [ed] Abdel H. El-Shaarawi; Walter W. Piegorsch, Chichester: John Wiley & Sons, 2002Chapter in book (Refereed)
    Download full text (pdf)
    Multicollinearity.pdf
  • 31.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Review of Brereton, R.G.: Applied Chemometrics for Scientists, Wiley 20072008In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274X, Vol. 103, no 483, p. 1317-1318Article, book review (Other academic)
  • 32.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Shrinkage regression2002In: Encyclopedia of Environmetrics / [ed] Abdel H. El-Shaarawi; Walter W. Piegorsch, Chichester: John Wiley & Sons, 2002, p. 1994-1998Chapter in book (Refereed)
    Download full text (pdf)
    Shrinkagereg.pdf
  • 33.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Small sample and selection bias effects in calibration under latent factor regression models2007In: Journal of Chemometrics, Vol. 21, p. 227-238Article in journal (Refereed)
  • 34.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Statistical Consulting2011In: International Encyclopedia of Statistical Science / [ed] Miodrag Lovric, Springer Berlin/Heidelberg, 2011, p. 1390-1392Chapter in book (Refereed)
  • 35.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Statistical Modelling by Exponential Families2019Book (Refereed)
    Abstract [en]

    This book is a readable, digestible introduction to exponential families, encompassing statistical models based on the most useful distributions in statistical theory, including the normal, gamma, binomial, Poisson, and negative binomial. Strongly motivated by applications, it presents the essential theory and then demonstrates the theory's practical potential by connecting it with developments in areas like item response analysis, social network models, conditional independence and latent variable structures, and point process models. Extensions to incomplete data models and generalized linear models are also included. In addition, the author gives a concise account of the philosophy of Per Martin-Löf in order to connect statistical modelling with ideas in statistical physics, including Boltzmann's law. Written for graduate students and researchers with a background in basic statistical inference, the book includes a vast set of examples demonstrating models for applications and exercises embedded within the text as well as at the ends of chapters.

  • 36.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Statistiska forskningsgruppen SFG under 70 år, 1948–2018: Sammanställning gjord av Rolf Sundberg i anledning av 70-årsjubileet, 7 nov. 20182018Other (Other (popular science, discussion, etc.))
    Abstract [sv]

    Förteckning årsvis över SFGs ordf, sekr/skattm (sammanslås 1959) och föreståndare (fr. 1992/93), samt parentetiska anmärkningar. Utförligare kommentarer i många fall finns i påföljande noter. 

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    SFGHistorik70år.pdf
  • 37.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Student's t — 100 år2008In: Qvintensen, no 4Article in journal (Other (popular science, discussion, etc.))
  • 38.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Urval ur ändliga populationer: Kompendium2010Other (Other academic)
    Abstract [sv]

       

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    Urvalskompendiet.pdf
  • 39.
    Sundberg, Rolf
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Castensson, Anja
    Uppsala universitet, Sweden.
    Jazin, Elena
    Uppsala universitet, Sweden.
    Statistical modeling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia2006In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 7, no 1, p. 130-144Article in journal (Refereed)
    Abstract [en]

    Aspects of experimental design, statistical modelling, and statistical inference in case--control real-time RT--PCR assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia-related. Real-time RT--PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required.A basic multivariate (for several genes jointly) random-effects analysis of covariance (MRANCOVA) model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first toaverage over the intra-individual replicates,  This has consequences for  the influence of plate effects.  Topics also discussed are testing for a significant mean disease effect, differential co-regulation, and the difficulty of identifying genes affected in only a subgroup of cases.

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    manuskript
  • 40.
    Sundberg, Rolf
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Feldmann, Uwe
    Saarland University, Germany.
    Exploratory factor analysis-Parameter estimation and scores prediction with high-dimensional data2016In: Journal of Multivariate Analysis, ISSN 0047-259X, E-ISSN 1095-7243, Vol. 148, p. 49-59Article in journal (Refereed)
    Abstract [en]

    In an approach aiming at high-dimensional situations, we first introduce a distribution-free approach to parameter estimation in the standard random factor model, that is shown to lead to the same estimating equations as maximum likelihood estimation under normality. The derivation is considerably simpler, and works equally well in the case of more variables than observations (p>n). We next concentrate on the latter case and show results of type:

    • Albeit factor loadings and specific variances cannot be precisely estimated unless n is large, this is not needed for the factor scores to be precise, but only that p is large;

    • A classical fixed point iteration method can be expected to converge safely and rapidly, provided p is large. A microarray data set, with p=2000 and n=22, is used to illustrate this theoretical result.

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    SundbergFeldmannJMVA2016
  • 41.
    Sundberg, Rolf
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Moberg, Anders
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Hind, Alistair
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 1: Theory2012In: Climate of the Past, ISSN 1814-9324, E-ISSN 1814-9332, Vol. 8, no 4, p. 1339-1353Article in journal (Refereed)
    Abstract [en]

    A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

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    fulltext
  • 42.
    Tyrcha, Joanna
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Statistical modelling and saddle point approximation of tail probabilities for accumulated splice loss in fibre optic networks2000In: J. Applied Statistics, ISSN 0266-4763, Vol. 27, no 2, p. 245-256Article in journal (Refereed)
  • 43.
    Tyrcha, Joanna
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Lindskog, Peter
    Sundström, Bernt
    Statistical modelling and saddle point approximation of tail probabilities for accumulated splice loss in fibre optic networks1998Report (Other academic)
  • 44. von Stein, Petra
    et al.
    Persson, Jan-Olov
    Stockholm University, Faculty of Science, Department of Mathematics. Matematisk statistik.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Multigene analysis can discriminate between ulcerative colitis, Crohn's disease and irritable bowel syndrome2008In: Gastroenterology, Vol. 134, no 7, p. 1869-1881Article in journal (Other (popular science, discussion, etc.))
  • 45.
    Yin, Li
    et al.
    Karolinska institutet, Sweden.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Wang, Xiaoqin
    Gävle högskola, Sweden.
    Rubin, Donald B.
    Harvard University, USA.
    Control of confounding through secondary samples2006In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 25, no 22, p. 3814-3825Article in journal (Refereed)
    Abstract [en]

    The control of confounding is essential in many statistical problems, especially in those that attempt to estimate exposure effects. In some cases, in addition to the ‘primary’ sample, there is another ‘secondary’ sample which, though having no direct information about the exposure effect, contains information about the confounding factors. The purpose of this article is to study the influence of the secondary sample on likelihood inference for the exposure effect. In particular, we investigate the interplay between the efficiency improvement and the possible bias introduced by the secondary sample as a function of the degree of confounding in the primary sample and the sizes of the primary and secondary samples. In the case of weak confounding, the secondary sample can only little improve estimation of the exposure effect, whereas with strong confounding the secondary sample can be much more useful. On the other hand, it will be more important to consider possible biasing effects in the latter case. For illustration, we use a formal example of a generalized linear model and a real example with sparse data from a case–control study of the association between gastric cancer and HM-CAP/Band 120. 

1 - 45 of 45
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