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【下载】Bayesian Statistics and Marketing~P.E.Rossi.Wiley.2005
kxjs2007
5052 8
2010-06-04
Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics) (Hardcover)

Peter E. Rossi (Author), Greg M. Allenby (Author), Rob McCulloch (Author)

Editorial Reviews

Review
"..an asset for business schools and marketing researchers." (Technometrics, May 2007)
"'Bayesian Statistics and Marketing' comes from three pioneers in the field of market research and fills a hole in the existing literature on the topic." (Journal of the American Statistical Association, December 2006)
"…extremely useful to both researchers and practitioners who are interested in understanding the power of these methods for solving important marketing problems." (Journal of Marketing, October 2006)
" This book deserves to be widely adopted by business schools, and widely read by more numerate marketing practitioners." (Short Book Reviews, April 2006)
" … valuable to marketing researchers and others working on related applications, especially if they use advanced logistic and probit models." (JRSSA, Vol. 169, No. 4, October 2006)
" … an excellent book for researchers in applied Bayesian statistics." (Journal of Applied Statistics, Vol. 33: 9, 1034, November 2006)
‘…an important study tool for potential practitioners or all those researchers who study Bayesian methods through "learning by doing" (Statistical Papers,48,2007)
Product Description
The 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 product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.
Written by the leading experts in the field, this unique book:

· Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.


· Provides a self-contained introduction to Bayesian methods.


· Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.


· Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies.



Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.

Product Details
  • Hardcover: 368 pages
  • Publisher: Wiley; 1 edition (January 11, 2006)
  • Language: English
  • ISBN-10: 0470863676
  • ISBN-13: 978-0470863671
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Bayesian Statistics and Marketing~P.E.Rossi.2005.pdf

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kxjs2007
2010-6-4 08:12:42
Contents
1 Introduction 1
1.1 A Basic Paradigm for Marketing Problems 2
1.2 A Simple Example 3
1.3 Benefits and Costs of the Bayesian Approach 4
1.4 An Overview of Methodological Material and Case Studies 6
1.5 Computing and This Book 6
Acknowledgements 8
2 Bayesian Essentials 9
2.0 Essential Concepts from Distribution Theory 9
2.1 The Goal of Inference and Bayes’ Theorem 13
2.2 Conditioning and the Likelihood Principle 15
2.3 Prediction and Bayes 15
2.4 Summarizing the Posterior 16
2.5 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators 17
2.6 Identification and Bayesian Inference 19
2.7 Conjugacy, Sufficiency, and Exponential Families 20
2.8 Regression and Multivariate Analysis Examples 21
2.9 Integration and Asymptotic Methods 35
2.10 Importance Sampling 37
2.11 Simulation Primer for Bayesian Problems 41
2.12 Simulation from Posterior of Multivariate Regression Model 45
3 Markov Chain Monte Carlo Methods 49
3.1 Markov Chain Monte Carlo Methods 50
3.2 A Simple Example: Bivariate Normal Gibbs Sampler 52
3.3 Some Markov Chain Theory 57
3.4 Gibbs Sampler 63
3.5 Gibbs Sampler for the Seemingly Unrelated Regression Model 65
3.6 Conditional Distributions and Directed Graphs 67
3.7 Hierarchical Linear Models 70
3.8 Data Augmentation and a Probit Example 75
3.9 Mixtures of Normals 79
3.10 Metropolis Algorithms 86
3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model 94
3.12 Hybrid Markov Chain Monte Carlo Methods 97
3.13 Diagnostics 99
4 Unit-Level Models and Discrete Demand 103
4.1 Latent Variable Models 104
4.2 Multinomial Probit Model 106
4.3 Multivariate Probit Model 116
4.4 Demand Theory and Models Involving Discrete Choice 122
5 Hierarchical Models for Heterogeneous Units 129
5.1 Heterogeneity and Priors 130
5.2 Hierarchical Models 132
5.3 Inference for Hierarchical Models 133
5.4 A Hierarchical Multinomial Logit Example 136
5.5 Using Mixtures of Normals 142
5.6 Further Elaborations of the Normal Model of Heterogeneity 154
5.7 Diagnostic Checks of the First-Stage Prior 155
5.8 Findings and Influence on Marketing Practice 156
6 Model Choice and Decision Theory 159
6.1 Model Selection 160
6.2 Bayes Factors in the Conjugate Setting 162
6.3 Asymptotic Methods for Computing Bayes Factors 163
6.4 Computing Bayes Factors Using Importance Sampling 165
6.5 Bayes Factors Using MCMC Draws 166
6.6 Bridge Sampling Methods 169
6.7 Posterior Model Probabilities with Unidentified Parameters 170
6.8 Chib’s Method 171
6.9 An Example of Bayes Factor Computation: Diagonal Multinomial Probit Models 173
6.10 Marketing Decisions and Bayesian Decision Theory 177
6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information 180
7 Simultaneity 185
7.1 A Bayesian Approach to Instrumental Variables 185
7.2 Structural Models and Endogeneity/Simultaneity 195
7.3 Nonrandom Marketing Mix Variables 200
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kxjs2007
2010-6-4 08:13:03
Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts 207
Background 207
Model 209
Data 214
Results 219
Discussion 222
R Implementation 224
Case Study 2: Modeling Interdependent Consumer Preferences 225
Background 225
Model 226
Data 229
Results 230
Discussion 235
R Implementation 235
Case Study 3: Overcoming Scale Usage Heterogeneity 237
Background 237
Model 240
Priors and MCMC Algorithm 244
Data 246
Discussion 251
R Implementation 252
Case Study 4: A Choice Model with Conjunctive Screening Rules 253
Background 253
Model 254
Data 255
Results 259
Discussion 264
R Implementation 266
Case Study 5: Modeling Consumer Demand for Variety 269
Background 269
Model 270
Data 271
Results 273
Discussion 273
R Implementation 277

Appendix A An Introduction to Hierarchical Bayes Modeling in R 279
A.1 Setting Up the R Environment 279
A.2 The R Language 285
A.3 Hierarchical Bayes Modeling – An Example 303
Appendix B A Guide to Installation and Use of bayesm 323
B.1 Installing bayesm 323
B.2 Using bayesm 323
B.3 Obtaining Help on bayesm 324
B.4 Tips on Using MCMC Methods 327
B.5 Extending and Adapting Our Code 327
B.6 Updating bayesm 327
References 335
Index 341
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jonck
2010-6-4 15:52:06
楼主好人啊，谢谢分享！
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holybed
2011-1-25 08:52:10
价钱公道，在米国买书太贵了。。。
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icceo
2011-3-14 22:38:58
good book, thanks a lot
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