Statistical Modelling and Regression Structures

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. 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. . . . . . . . . . . . . . . . . . . . . . ."

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