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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The Smooth Complex Logarithm and Quasi-Periodic Models.- P-spline Varying Coefficient Models for Complex Data.- Penalized Splines, Mixed Models and Bayesian Ideas.- Bayesian Linear Regression #x2014; Different Conjugate Models and Their (In)Sensitivity to Prior-Data Conflict.- An Efficient Model Averaging Procedure for Logistic Regression Models Using a Bayesian Estimator with Laplace Prior.- Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA.- Data Augmentation and MCMC for Binary and Multinomial Logit Models.- Generalized Semiparametric Regression with Covariates Measured with Error.- Determinants of the Socioeconomic and Spatial Pattern of Undernutrition by Sex in India: A Geoadditive Semi-parametric Regression Approach.- Boosting for Estimating Spatially Structured Additive Models.- Generalized Linear Mixed Models Based on Boosting.- Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students.- Graphical Chain Models and their Application.- Indirect Comparison of Interaction Graphs.- Modelling, Estimation and Visualization of Multivariate Dependence for High-frequency Data.- Ordinal- and Continuous-Response Stochastic Volatility Models for Price Changes: An Empirical Comparison.- Copula Choice with Factor Credit Portfolio Models.- Penalized Estimation for Integer Autoregressive Models.- Bayesian Inference for a Periodic Stochastic Volatility Model of Intraday Electricity Prices.- Online Change-Point Detection in Categorical Time Series.- Multiple Linear Panel Regression with Multiplicative Random Noise.- A Note on Using Multiple Singular Value Decompositions to Cluster Complex Intracellular Calcium Ion Signals.- On the self-regularization property of the EM algorithm for Poisson inverse problems.- SequentialDesign of Computer Experiments for Constrained Optimization.
