LEADER 05881nam 2200793Ia 450 001 9910825632103321 005 20230829010425.0 010 $a9786613622150 010 $a9781280592324 010 $a128059232X 010 $a9780470863695 010 $a0470863692 010 $a9780470863688 010 $a0470863684 035 $a(CKB)1000000000327278 035 $a(EBL)792774 035 $a(OCoLC)793995918 035 $a(SSID)ssj0000353879 035 $a(PQKBManifestationID)11236642 035 $a(PQKBTitleCode)TC0000353879 035 $a(PQKBWorkID)10301973 035 $a(PQKB)10031008 035 $a(MiAaPQ)EBC792774 035 $a(Au-PeEL)EBL792774 035 $a(CaPaEBR)ebr10631330 035 $a(CaONFJC)MIL362215 035 $a(Perlego)2751705 035 $a(EXLCZ)991000000000327278 100 $a20050607e20062005 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian statistics and marketing /$fPeter E. Rossi, Greg M. Allenby, Robert McCulloch 205 $aReprinted with corrections. 210 $aChichester, England $cJ. Wiley$d2006,c2005 215 $a1 online resource (372 p.) 225 0$aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470863671 311 08$a0470863676 320 $aIncludes bibliographical references and index. 327 $aBayesian Statistics and Marketing; Contents; 1 Introduction; 1.1 A Basic Paradigm for Marketing Problems; 1.2 A Simple Example; 1.3 Benefits and Costs of the Bayesian Approach; 1.4 An Overview of Methodological Material and Case Studies; 1.5 Computing and This Book; Acknowledgements; 2 Bayesian Essentials; 2.0 Essential Concepts from Distribution Theory; 2.1 The Goal of Inference and Bayes' Theorem; 2.2 Conditioning and the Likelihood Principle; 2.3 Prediction and Bayes; 2.4 Summarizing the Posterior; 2.5 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 327 $a2.6 Identification and Bayesian Inference 2.7 Conjugacy, Sufficiency, and Exponential Families; 2.8 Regression and Multivariate Analysis Examples; 2.9 Integration and Asymptotic Methods; 2.10 Importance Sampling; 2.11 Simulation Primer for Bayesian Problems; 2.12 Simulation from the Posterior of the Multivariate Regression Model; 3 Markov Chain Monte Carlo Methods; 3.1 Markov Chain Monte Carlo Methods; 3.2 A Simple Example: Bivariate Normal Gibbs Sampler; 3.3 Some Markov Chain Theory; 3.4 Gibbs Sampler; 3.5 Gibbs Sampler for the Seemingly Unrelated Regression Model 327 $a3.6 Conditional Distributions and Directed Graphs 3.7 Hierarchical Linear Models; 3.8 Data Augmentation and a Probit Example; 3.9 Mixtures of Normals; 3.10 Metropolis Algorithms; 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model; 3.12 Hybrid Markov Chain Monte Carlo Methods; 3.13 Diagnostics; 4 Unit-Level Models and Discrete Demand; 4.1 Latent Variable Models; 4.2 Multinomial Probit Model; 4.3 Multivariate Probit Model; 4.4 Demand Theory and Models Involving Discrete Choice; 5 Hierarchical Models for Heterogeneous Units; 5.1 Heterogeneity and Priors 327 $a5.2 Hierarchical Models 5.3 Inference for Hierarchical Models; 5.4 A Hierarchical Multinomial Logit Example; 5.5 Using Mixtures of Normals; 5.6 Further Elaborations of the Normal Model of Heterogeneity; 5.7 Diagnostic Checks of the First-Stage Prior; 5.8 Findings and Influence on Marketing Practice; 6 Model Choice and Decision Theory; 6.1 Model Selection; 6.2 Bayes Factors in the Conjugate Setting; 6.3 Asymptotic Methods for Computing Bayes Factors; 6.4 Computing Bayes Factors Using Importance Sampling; 6.5 Bayes Factors Using MCMC Draws; 6.6 Bridge Sampling Methods 327 $a6.7 Posterior Model Probabilities with Unidentified Parameters 6.8 Chib's Method; 6.9 An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models; 6.10 Marketing Decisions and Bayesian Decision Theory; 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information; 7 Simultaneity; 7.1 A Bayesian Approach to Instrumental Variables; 7.2 Structural Models and Endogeneity/Simultaneity; 7.3 Nonrandom Marketing Mix Variables; Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts; Background; Model; Data; Results 327 $aDiscussion 330 $aThe past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on 410 0$aWiley Series in Probability and Statistics 606 $aBayesian statistical decision theory 606 $aMarketing research$xMathematical models 606 $aMarketing$xMathematical models 615 0$aBayesian statistical decision theory. 615 0$aMarketing research$xMathematical models. 615 0$aMarketing$xMathematical models. 676 $a658.8 676 $a658.83015118 700 $aRossi$b Peter E$g(Peter Eric),$f1955-$0503490 701 $aAllenby$b Greg M$g(Greg Martin),$f1956-$0503491 701 $aMcCulloch$b Robert E$g(Robert Edward)$01723177 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910825632103321 996 $aBayesian statistics and marketing$94124205 997 $aUNINA