Verfügbarkeit: Bayesian statistics and marketing | Technische Universität München

Bayesian statistics and marketing:

Gespeichert in:

Bibliographische Detailangaben
Beteiligte Personen:	Rossi, Peter E. 1955- (VerfasserIn), Allenby, Greg M. 1956- (VerfasserIn), McCulloch, Robert E. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Chichester [u.a.] Wiley 2006
Ausgabe:	Repr. with corr.
Schriftenreihe:	Wiley series in probability and statistics
Schlagwörter:	Mathematisches Modell Marketing research > Mathematical models Marketing > Mathematical models Bayesian statistical decision theory R > Programm Bayes-Verfahren Bayer-Verfahren Marketingforschung Bayes-Entscheidungstheorie Marketing
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015573198&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	X, 348 S. graph. Darst.
ISBN:	0470863676 9780470863671

Internformat

MARC


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Datensatz im Suchindex

DE-BY-TUM_call_number	1002 WIR 803f 2011 B 2765
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adam_text	Contents 1 Introduction 1 1.1 A Basic Paradigm lor Marketing Problems 2 1.2 Λ Simple Kxamplc 3 1.3 Benefits and Costs ofthe Bayesian Approach 4 1.4 An Overview of Methodological Material and (lase Studies 6 1.5 Computing and This Rook 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 or 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 2.1 1 Simulation Primer for Bayesian Problems 41 2.12 Simulation from the Posterior of the 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 viii CONTENTS 3.6 Conditional Distributions and Directed Graphs 67 3.7 Hierarchical Linear Models 70 3.8 Data Augmentation and a Probit Hxample 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 Hxample 136 5.5 Using Mixtures of Normals 142 5.6 Further Klaborations 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 F.xample of Bayes Factor Computation: Diagonal Multinomial Probit Models 173 6.10 Marketing Decisions and Bayesian Decision Theory 177 6.11 An FvXample of Bayesian Decision Theory: Valuing Llousehold Purchase Information 180 7 Simultaneity 185 7.1 A Bayesian Approach to Instrumental Variables 185 CONTENTS ix 7.2 Structural Models and Hndogeneity/Simiiltaneity 195 7.3 Nonrandom Marketing Mix Variables 200 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 χ CONTENTS Appendix A An Introduction to Hierarchical Bayes Modeling in R 279 A.I Setting Lip the R Ivnvironmeni 279 A. 2 The R Language 285 A.3 I Iieraichical Bayes Modeling An Kxample 303 Appendix В A Guide to Installation and Use of baye sm 323 B.I Installing bayesm 323 B.2 Using bayesm 323 B.3 Obtaining I Ielp on bayesm 324 B.4 Tips on Using MCMC Methods 327 B.5 Hxtending and Adapting Our Code 327 B.6 Updating bayesm 327 References 335 Index 341
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author	Rossi, Peter E. 1955- Allenby, Greg M. 1956- McCulloch, Robert E.
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spellingShingle	Rossi, Peter E. 1955- Allenby, Greg M. 1956- McCulloch, Robert E. Bayesian statistics and marketing Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm (DE-588)4705956-4 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Marketing (DE-588)4037589-4 gnd
subject_GND	(DE-588)4705956-4 (DE-588)4204326-8 (DE-588)4114528-8 (DE-588)4210428-2 (DE-588)4200055-5 (DE-588)4144220-9 (DE-588)4037589-4
title	Bayesian statistics and marketing
title_auth	Bayesian statistics and marketing
title_exact_search	Bayesian statistics and marketing
title_full	Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch
title_fullStr	Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch
title_full_unstemmed	Bayesian statistics and marketing Peter E. Rossi ; Greg M. Allenby ; Robert McCulloch
title_short	Bayesian statistics and marketing
title_sort	bayesian statistics and marketing
topic	Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm (DE-588)4705956-4 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayer-Verfahren (DE-588)4210428-2 gnd Marketingforschung (DE-588)4200055-5 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Marketing (DE-588)4037589-4 gnd
topic_facet	Mathematisches Modell Marketing research Mathematical models Marketing Mathematical models Bayesian statistical decision theory R Programm Bayes-Verfahren Bayer-Verfahren Marketingforschung Bayes-Entscheidungstheorie Marketing
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015573198&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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