05477nam 22007334a 450 991101945940332120200520144314.09786610287703978128028770112802877059780470092392047009239497804700923850470092386(CKB)1000000000239293(EBL)242953(SSID)ssj0000109309(PQKBManifestationID)11129594(PQKBTitleCode)TC0000109309(PQKBWorkID)10045545(PQKB)10889214(MiAaPQ)EBC242953(OCoLC)85820731(Perlego)2751776(EXLCZ)99100000000023929320050222d2005 uy 0engur|n|---|||||txtccrBayesian models for categorical data /Peter CongdonChichester ;New York Wileyc20051 online resource (448 p.)Wiley series in probability and statisticsDescription based upon print version of record.9780470092378 0470092378 Includes bibliographical references and index.Bayesian 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 choice2.3 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 outliers3.4 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 models3.7.4 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 tables4.6 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 transformations5.5.3 Beta mixture on cumulative densitiesThe 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 daWiley series in probability and statistics.Bayesian statistical decision theoryMonte Carlo methodMarkov processesMultivariate analysisBayesian statistical decision theory.Monte Carlo method.Markov processes.Multivariate analysis.519.5/42Congdon P145037MiAaPQMiAaPQMiAaPQBOOK9911019459403321Bayesian models for categorical data737826UNINA