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Bayesian statistics and marketing

Author: Rossi, Peter E. ; Allenby, Greg M. ; McCulloch, Robert Series: Wiley series in probability and statistics Publisher: Wiley, 2005.Language: EnglishDescription: 348 p. ; 24 cm.ISBN: 0470863676Type of document: Book Online Access: Click here Note: Doriot: for 2013-2014 coursesBibliography/Index: Includes bibliographical references and index
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Book Asia Campus
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Print HF5415.2 .R67 2005
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900217754
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Book Asia Campus
Textbook Collection (PhD)
Print HF5415.2 .R67 2005
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001211238
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Doriot: for 2013-2014 courses

Includes bibliographical references and index

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Bayesian 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 2.6 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 3.2 3.3 3.4 3.5 Markov Chain Monte Carlo Methods A Simple Example: Bivariate Normal Gibbs Sampler Some Markov Chain Theory Gibbs Sampler Gibbs Sampler for the Seemingly Unrelated Regression Model 1 2 3 4 6 6 8 9 9 13 15 15 16 17 19 20 21 35 37 41 45 49 50 52 57 63 65 viii CONTENTS 3.6 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 67 70 75 79 86 94 97 99 103 104 106 116 122 129 130 132 133 136 142 154 155 156 159 160 162 163 165 166 169 170 171 173 177 180 185 185 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 5.2 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 6.7 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 CONTENTS 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 Discussion R Implementation Case Study 2: Modeling Interdependent Consumer Preferences Background Model Data Results Discussion R Implementation Case Study 3: Overcoming Scale Usage Heterogeneity Background Model Priors and MCMC Algorithm Data Discussion R Implementation Case Study 4: A Choice Model with Conjunctive Screening Rules Background Model Data Results Discussion R Implementation Case Study 5: Modeling Consumer Demand for Variety Background Model Data Results Discussion R Implementation 207 207 209 214 219 222 224 225 225 226 229 230 235 235 237 237 240 244 246 251 252 253 253 254 255 259 264 266 269 269 270 271 273 273 277 195 200 x CONTENTS 279 279 285 303 323 323 323 324 327 327 327 335 341 Appendix A An Introduction to Hierarchical Bayes Modeling in R A.1 Setting Up the R Environment A.2 The R Language A.3 Hierarchical Bayes Modeling - An Example Appendix B A Guide to Installation and Use of bayesm B.1 Installing bayesm B.2 Using bayesm B.3 Obtaining Help on bayesm B.4 Tips on Using MCMC Methods B.5 Extending and Adapting Our Code B.6 Updating bayesm References Index

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