LEADER 05477nam 22007334a 450 001 9911019459403321 005 20200520144314.0 010 $a9786610287703 010 $a9781280287701 010 $a1280287705 010 $a9780470092392 010 $a0470092394 010 $a9780470092385 010 $a0470092386 035 $a(CKB)1000000000239293 035 $a(EBL)242953 035 $a(SSID)ssj0000109309 035 $a(PQKBManifestationID)11129594 035 $a(PQKBTitleCode)TC0000109309 035 $a(PQKBWorkID)10045545 035 $a(PQKB)10889214 035 $a(MiAaPQ)EBC242953 035 $a(OCoLC)85820731 035 $a(Perlego)2751776 035 $a(EXLCZ)991000000000239293 100 $a20050222d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian models for categorical data /$fPeter Congdon 210 $aChichester ;$aNew York $cWiley$dc2005 215 $a1 online resource (448 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470092378 311 08$a0470092378 320 $aIncludes bibliographical references and index. 327 $aBayesian Models for Categorical Data; Contents; Preface; Chapter 1 Principles of Bayesian Inference; 1.1 Bayesian updating; 1.2 MCMC techniques; 1.3 The basis for MCMC; 1.4 MCMC sampling algorithms; 1.5 MCMC convergence; 1.6 Competing models; 1.7 Setting priors; 1.8 The normal linear model and generalized linear models; 1.9 Data augmentation; 1.10 Identifiability; 1.11 Robustness and sensitivity; 1.12 Chapter themes; References; Chapter 2 Model Comparison and Choice; 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria; 2.2 Formal Bayes model choice 327 $a2.3 Marginal likelihood and Bayes factor approximations2.4 Predictive model choice and checking; 2.5 Posterior predictive checks; 2.6 Out-of-sample cross-validation; 2.7 Penalized deviances from a Bayes perspective; 2.8 Multimodel perspectives via parallel sampling; 2.9 Model probability estimates from parallel sampling; 2.10 Worked example; References; Chapter 3 Regression for Metric Outcomes; 3.1 Introduction: priors for the linear regression model; 3.2 Regression model choice and averaging based on predictor selection; 3.3 Robust regression methods: models for outliers 327 $a3.4 Robust regression methods: models for skewness and heteroscedasticity3.5 Robustness via discrete mixture models; 3.5.1 Complete data representation; 3.5.2 Identification issues; 3.5.3 Dirichlet process mixture models; 3.6 Non-linear regression effects via splines and other basis functions; 3.6.1 Penalized random effects for spline coefficients; 3.6.2 Basis function regression; 3.6.3 Special spline functions; 3.7 Dynamic linear models and their application in non-parametric regression; 3.7.1 Some common forms of DLM; 3.7.2 Robust errors; 3.7.3 General additive models 327 $a3.7.4 Alternative smoothness priorsExercises; References; Chapter 4 Models for Binary and Count Outcomes; 4.1 Introduction: discrete model likelihoods vs. data augmentation; 4.1.1 Count data; 4.1.2 Binomial and binary data; 4.2 Estimation by data augmentation: the Albert-Chib method; 4.2.1 Other augmented data methods; 4.3 Model assessment: outlier detection and model checks; 4.3.1 Model assessment: predictive model selection and checks; 4.4 Predictor selection in binary and count regression; 4.5 Contingency tables 327 $a4.6 Semi-parametric and general additive models for binomial and count responses4.6.1 Robust and adaptive non-parametric regression; 4.6.2 Other approaches to non-linearity; Exercises; References; Chapter 5 Further Questions in Binomial and Count Regression; 5.1 Generalizing the Poisson and binomial: overdispersion and robustness; 5.2 Continuous mixture models; 5.2.1 Modified exponential families; 5.3 Discrete mixtures; 5.4 Hurdle and zero-inflated models; 5.5 Modelling the link function; 5.5.1 Discrete (DPP mixture); 5.5.2 Parametric link transformations 327 $a5.5.3 Beta mixture on cumulative densities 330 $aThe use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes.* Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data).* Considers missing da 410 0$aWiley series in probability and statistics. 606 $aBayesian statistical decision theory 606 $aMonte Carlo method 606 $aMarkov processes 606 $aMultivariate analysis 615 0$aBayesian statistical decision theory. 615 0$aMonte Carlo method. 615 0$aMarkov processes. 615 0$aMultivariate analysis. 676 $a519.5/42 700 $aCongdon$b P$0145037 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019459403321 996 $aBayesian models for categorical data$9737826 997 $aUNINA