Gerhard Tutz – författare

Since our first edition of this book, many developments in statistical mod­ elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv­ ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

This volume presents the published Proceedings of the joint meeting of GUM92 and the 7th International Workshop on Statistical Modelling, held in Munich, Germany from 13 to 17 July 1992. The meeting aimed to bring together researchers interested in the development and applications of generalized linear modelling in GUM and those interested in statistical modelling in its widest sense. This joint meeting built upon the success of previous workshops and GUM conferences. Previous GUM conferences were held in London and Lancaster, and a joint GUM Conference/4th Modelling Workshop was held in Trento. (The Proceedings of previous GUM conferences/Statistical Modelling Workshops are available as numbers 14 , 32 and 57 of the Springer Verlag series of Lecture Notes in Statistics). Workshops have been organized in Innsbruck, Perugia, Vienna, Toulouse and Utrecht. (Proceedings of the Toulouse Workshop appear as numbers 3 and 4 of volume 13 of the journal Computational Statistics and Data Analysis). Much statistical modelling is carried out using GUM, as is apparent from many of the papers in these Proceedings. Thus the Programme Committee were also keen on encouraging papers which addressed problems which are not only of practical importance but which are also relevant to GUM or other software development. The Programme Committee requested both theoretical and applied papers. Thus there are papers in a wide range of practical areas, such as ecology, breast cancer remission and diabetes mortality, banking and insurance, quality control, social mobility, organizational behaviour.

This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

Since our first edition of this book, many developments in statistical mod­ elling based on generalized linear models have been published, and our primary aim is to bring the book up to date. Naturally, the choice of these recent developments reflects our own teaching and research interests. The new organization parallels that of the first edition. We try to motiv­ ate and illustrate concepts with examples using real data, and most data sets are available on http:/ fwww. stat. uni-muenchen. de/welcome_e. html, with a link to data archive. We could not treat all recent developments in the main text, and in such cases we point to references at the end of each chapter. Many changes will be found in several sections, especially with those connected to Bayesian concepts. For example, the treatment of marginal models in Chapter 3 is now current and state-of-the-art. The coverage of nonparametric and semiparametric generalized regression in Chapter 5 is completely rewritten with a shift of emphasis to linear bases, as well as new sections on local smoothing approaches and Bayesian inference. Chapter 6 now incorporates developments in parametric modelling of both time series and longitudinal data. Additionally, random effect models in Chapter 7 now cover nonparametric maximum likelihood and a new section on fully Bayesian approaches. The modifications and extensions in Chapter 8 reflect the rapid development in state space and hidden Markov models.

The topics covered include the binary Rasch model, its extensions and alternative binary models, ordinal models and their extensions that account for response styles, the thresholds model, classical test theory, response models for count data, differential item functioning, and explanatory item response models.

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

This book focuses on statistical methods for the analysis of discrete failure times. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale.

Dieses Arbeitsbuch ergänzt perfekt das Lehrbuch Fahrmeir/Künstler/Pigeot/Tutz: Statistik - Der Weg zur Datenanalyse. Es dient damit der Vertiefung und der Einübung des im Lehrbuch vermittelten Stoffes zur Wahrscheinlichkeitsrechnung, deskriptiven und induktiven Statistik.

Dieses Lehrbuch liefert eine umfassende Darstellung der deskriptiven und induktiven Statistik sowie moderner Methoden der explorativen Datenanalyse. Dabei stehen inhaltliche Motivation, Interpretation und Verständnis der Methoden im Vordergrund. Unterstützt werden diese durch zahlreiche Grafiken und Anwendungsbeispiele, die auf realen Daten basieren, sowie passende exemplarische R-Codes und Datensätze. Die im Buch beschriebenen Ergebnisse können außerdem anhand der online zur Verfügung stehenden Materialien reproduziert sowie um eigene Analysen ergänzt werden. Eine kurze Einführung in die freie Programmiersprache R ist ebenfalls enthalten. Hervorhebungen erhöhen die Lesbarkeit und Übersichtlichkeit. Das Buch eignet sich als vorlesungsbegleitende Lektüre, aber auch zum Selbststudium.Für die 9. Auflage wurde das Buch inhaltlich überarbeitet und ergänzt. Leserinnen und Leser erhalten nun in der Springer-Nature-Flashcards-App zusätzlich kostenfreien Zugriff auf über 100 exklusive Lernfragen, mit denen sie ihr Wissen überprüfen können.

Thecollectedcontributionscontainedwithinthisbookhavebeenwrittenbyfriends andcolleaguestoacknowledgeLudwigFahrmeir'swidespreadandimportantimpact onStatisticsasascience,whilecelebratinghis65thBirthday. Asayoungstudent,LudwigstartedhiscareerasaMathematician,buthequickly turnedintoarisingandshiningstarwithintheGermanandinternationalStatistics community. HesoonobtainedbothhisPhDandhisHabilitationattheTechnical UniversityofMunich. AfterashortperiodasavisitingprofessorattheUniversity ofDortmund,hereturnedtohishomelandBavariaandwasappointedFullProfessor ofStatisticsattheUniversityofRegensburg,attheageof32. Someyearslater,hemovedtothecapitalofBavariaandbecameProfessoratthe DepartmentofStatisticsattheUniversityofMunich. Hisappointmenthadsigni?cant impactontheDepartmentsince,soonafterhisarrival,Ludwigstartedaninitiative toestablishacollaborativeresearchcenteronthe"StatisticalAnalysisofDiscrete Structures. "Afterasuccessfulapplicationforinitialfunding,furtherfundingwas extendedseveraltimes,untiltheresearchcenterreachedthemaximumperiodfor fundingin2006. Duringthecompleteduration,Ludwigservedasaspeakerofthe researchcenterand-tociteoneofthe?nalreferees-"manageditinaneasyand ef?cientwayandcontributedseveralimportantresults." Duringthelastfortyyears,Ludwig'sworkhashadtremendousimpactontheS- tisticscommunity. Hewasamongthe?rstresearcherstorecognizetheimportance ofgeneralizedlinearmodelsandcontributedinaseriesofpaperstothetheoretical backgroundofthatmodelclass. Hisinterestinstatisticalmodellingledtothe- ganizationofaworkshopon"StatisticalModellingandGeneralizedLinearModels (GLIM)"inMunichin1992andculminatedinthehighlycitedmonographon"M- tivariateStatisticalModellingBasedonGeneralizedLinearModels"thatsawtwo printingsandremainstobeakeyreferenceonappliedstatisticalmodellingutilizing generalizedlinearmodels. Ludwigalsohadgreatin?uenceonthecreationofthe StatisticalModellingSociety,andiscurrentlyontheadvisoryboardofthecor- spondingjournalon"StatisticalModelling. "Boththesocietyandjournalemerged outoftheearlyGLIMworkshopsandproceedings. v vi Foreword Ofcourse,Ludwig'sworkisde?nitelynotrestrictedtogeneralizedlinearmodels but-onthecontrary-spansawiderangeofmodernStatistics. Heco-authoredor co-editedseveralmonographs,e. g. onMultivariateStatistics,StochasticProcesses, MeasurementofCreditRisks,aswellaspopulartextbooksonRegressionandan IntroductiontoStatistics.Hisrecentresearchcontributionsaremostlyconcentrated insemiparametricregressionandspatialstatisticswithinaBayesianframework. When?rstcirculatingtheideaofaFestschriftforthecelebrationofLudwig's 65thbirthday,allpotentialcontributorswereextremelypositive,manyimmediately agreeingtocontribute. ThesereactionsatesttoLudwig'shighpersonalandp- fessionalappreciationinthestatisticalcommunity. Thefarreachingandvarietyof subjectscoveredwithinthesecontributionsalsorepresentsLudwig'sbroadinterest andimpactinmanybranchesofmodernStatistics. BotheditorsofthisFestschriftwereluckyenoughtoworkwithLudwigatseveral occasionsandinparticularearlyintheircareersasPhDstudentsandPostDocs. His personalandprofessionalmentorshipandhisstrongcommitmentmadehimaperfect supervisorandhispatient,con?dentandencouragingworkingstylewillalwaysbe rememberedbyallofhisstudentsandcolleagues. Ludwigalwaysprovidedafriendly workingenvironmentthatmadeitapleasureandanhonortobeapartofhisworking group. WeareproudtobeabletosaythatLudwigismuchmorethanacolleague butturnedintoafriendforbothofus.OldenburgandMunich,January2010 ThomasKneib,GerhardTutz Acknowledgements Theeditorswouldliketoexpresstheirgratitudeto * allauthorsofthisvolumefortheiragreementtocontributeandtheireasyco- erationatseveralstagesofputtingtogetherthe?nalversionoftheFestschrift. * JohannaBrandt,JanGertheiss,AndreasGroll,FelixHeinzl,SebastianPetry,Jan UlbrichtandStephanieRubenbauerfortheirinvaluablecontributionsinproof- A readingandcorrectionofthepapers,aswellasinsolvingseveralLTX-related E problems. *swidespreadandimportantimpact onStatisticsasascience,whilecelebratinghis65thBirthday. Asayoungstudent,LudwigstartedhiscareerasaMathematician,buthequickly turnedintoarisingandshiningstarwithintheGermanandinternationalStatistics community. HesoonobtainedbothhisPhDandhisHabilitationattheTechnical UniversityofMunich. AfterashortperiodasavisitingprofessorattheUniversity ofDortmund,hereturnedtohishomelandBavariaandwasappointedFullProfessor ofStatisticsattheUniversityofRegensburg,attheageof32. Someyearslater,hemovedtothecapitalofBavariaandbecameProfessoratthe DepartmentofStatisticsattheUniversityofMunich. Hisappointmenthadsigni?cant impactontheDepartmentsince,soonafterhisarrival,Ludwigstartedaninitiative toestablishacollaborativeresearchcenteronthe"StatisticalAnalysisofDiscrete Structures."Afterasuccessfulapplicationforinitialfunding,furtherfundingwas extendedseveraltimes,untiltheresearchcenterreachedthemaximumperiodfor fundingin2006. Duringthecompleteduration,Ludwigservedasaspeakerofthe researchcenterand-tociteoneofthe?nalreferees-"manageditinaneasyand ef?cientwayandcontributedseveralimportantresults. " Duringthelastfortyyears,Ludwig'sworkhashadtremendousimpactontheS- tisticscommunity. Hewasamongthe?rstresearcherstorecognizetheimportance ofgeneralizedlinearmodelsandcontributedinaseriesofpaperstothetheoretical backgroundofthatmodelclass. Hisinterestinstatisticalmodellingledtothe- ganizationofaworkshopon"StatisticalModellingandGeneralizedLinearModels (GLIM)"inMunichin1992andculminatedinthehighlycitedmonographon"M- tivariateStatisticalModellingBasedonGeneralizedLinearModels"thatsawtwo printingsandremainstobeakeyreferenceonappliedstatisticalmodellingutilizing generalizedlinearmodels. Ludwigalsohadgreatin?uenceonthecreationofthe StatisticalModellingSociety,andiscurrentlyontheadvisoryboardofthecor- spondingjournalon"StatisticalModelling. "Boththesocietyandjournalemerged outoftheearlyGLIMworkshopsandproceedings. v vi Foreword Ofcourse,Ludwig'sworkisde?nitelynotrestrictedtogeneralizedlinearmodels but-onthecontrary-spansawiderangeofmodernStatistics.Heco-authoredor co-editedseveralmonographs,e. g. onMultivariateStatistics,StochasticProcesses, MeasurementofCreditRisks,aswellaspopulartextbooksonRegressionandan IntroductiontoStatistics. Hisrecentresearchcontributionsaremostlyconcentrated insemiparametricregressionandspatialstatisticswithinaBayesianframework. When?rstcirculatingtheideaofaFestschriftforthecelebrationofLudwig's 65thbirthday,allpotentialcontributorswereextremelypositive,manyimmediately agreeingtocontribute. ThesereactionsatesttoLudwig'shighpersonalandp- fessionalappreciationinthestatisticalcommunity. Thefarreachingandvarietyof subjectscoveredwithinthesecontributionsalsorepresentsLudwig'sbroadinterest andimpactinmanybranchesofmodernStatistics. BotheditorsofthisFestschriftwereluckyenoughtoworkwithLudwigatseveral occasionsandinparticularearlyintheircareersasPhDstudentsandPostDocs. His personalandprofessionalmentorshipandhisstrongcommitmentmadehimaperfect supervisorandhispatient,con?dentandencouragingworkingstylewillalwaysbe rememberedbyallofhisstudentsandcolleagues. Ludwigalwaysprovidedafriendly workingenvironmentthatmadeitapleasureandanhonortobeapartofhisworking group.WeareproudtobeabletosaythatLudwigismuchmorethanacolleague butturnedintoafriendforbothofus. OldenburgandMunich,January2010 ThomasKneib,GerhardTutz Acknowledgements Theeditorswouldliketoexpresstheirgratitudeto * allauthorsofthisvolumefortheiragreementtocontributeandtheireasyco- erationatseveralstagesofputtingtogetherthe?nalversionoftheFestschrift. * JohannaBrandt,JanGertheiss,AndreasGroll,FelixHeinzl,SebastianPetry,Jan UlbrichtandStephanieRubenbauerfortheirinvaluablecontributionsinproof- A readingandcorrectionofthepapers,aswellasinsolvingseveralLTX-related E problems. * theSpringerVerlagforagreeingtopublishthisFestschriftandinparticularNils- PeterThomas,AliceBlanck and FrankHolzwarthfor the smooth collabo- tion in preparing th emanuscript. vii Contents ListofContributors...xix TheSmoothComplexLogarithmandQuasi-PeriodicModels ...1 PaulH. C. Eilers 1 Foreword...1 2 Introduction...1 3 DataandModels...2 3. 1 TheBasicModel...3 3. 2 SplinesandPenalties...3 3. 3 StartingValues...7 3. 4 SimpleTrendCorrectionandPriorTransformation...8 3. 5 AComplexSignal...8 3. 6 Non-normalDataandCascadedLinks...10 3. 7 AddingHarmonics...11 4 MoretoExplore...12 5 Discussion...15 References...17 P-splineVaryingCoef?cientModelsforComplexData...19 BrianD. Marx 1 Introduction ...19 2 "LargeScale"VCM,withoutBack?tting...22 3 NotationandSnapshotofaSmoothingTool:B-splines...24 3. 1 GeneralKnotPlacement...25 3. 2 SmoothingtheKTBData...25 4 UsingB-splinesforVaryingCoef?cientModels...26 5 P-splineSnapshot:Equally-SpacedKnots&Penalization...28 5. 1 P-splinesforAdditiveVCMs...30 5. 2 StandardErrorBands...30 6 OptimallyTuningP-splines ...31 7 MoreKTBResults...33 8 ExtendingP-VCMintotheGeneralizedLinearModel...33 9 Two-dimensionalVaryingCoef?cientModels...36 ix x Contents 9. 1 Mechanicsof2D-VCMthroughExample ...37 9. 2 VCMsandPenaltiesasArrays...39 9. 3 Ef?cientComputationUsingArrayRegression...40 10 DiscussionTowardMoreComplexVCMs...41 References...42 PenalizedSplines,MixedModelsandBayesianIdeas...45 .. GoranKauermann 1 Introduction...45 2 NotationandPenalizedSplinesasLinearMixedModels...46 3 Classi?cationwithMixedModels...48 4 VariableSelectionwithSimplePriors...50 4. 1 MarginalAkaikeInformationCriterion...50 4. 2 ComparisoninLinearModels...