Bayesian Statistics and Marketing

• Autore: Rossi Peter E
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 2nd ed.
• Descrizione fisica: 1 online resource (402 pages)
• Disciplina: 658.83015118
• Altri autori: AllenbyGreg M MisraSanjog
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Chapter 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 Approximate Bayes Methods and This Book -- 1.6 Computing and This Book -- Acknowledgments -- Chapter 2 Bayesian Essentials -- 2.1 Essential Concepts from Distribution Theory -- 2.2 The Goal of Inference and Bayes Theorem -- 2.2.1 Bayes Theorem -- 2.3 Conditioning and the Likelihood Principle -- 2.4 Prediction and Bayes -- 2.5 Summarizing the Posterior -- 2.6 Decision Theory, Risk, and the Sampling Properties of Bayes Estimators -- 2.7 Identification and Bayesian Inference -- 2.8 Conjugacy, Sufficiency, and Exponential Families -- 2.9 Regression and Multivariate Analysis Examples -- 2.9.1 Multiple Regression -- 2.9.2 Assessing Priors for Regression Models -- 2.9.3 Bayesian Inference for Covariance Matrices -- 2.9.4 Priors and the Wishart Distribution -- 2.9.5 Multivariate Regression -- 2.9.6 The Limitations of Conjugate Priors -- 2.10 Integration and Asymptotic Methods -- 2.11 Importance Sampling -- 2.11.1 GHK Method for Evaluation of Certain Integrals of MVN -- 2.12 Simulation Primer for Bayesian Problems -- 2.12.1 Uniform, Normal, and Gamma Generation -- 2.12.2 Truncated Distributions -- 2.12.3 Multivariate Normal and Student t Distributions -- 2.12.4 The Wishart and Inverted Wishart Distributions -- 2.12.5 Multinomial Distributions -- 2.12.6 Dirichlet Distribution -- 2.13 Simulation from Posterior of Multivariate Regression Model -- Chapter 3 MCMC Methods -- 3.1 MCMC 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 SUR Regression Model -- 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 -- 3.9.1 Identification in Normal Mixtures -- 3.9.2 Performance of the Unconstrained Gibbs Sampler -- 3.10 Metropolis Algorithms -- 3.10.1 Independence Metropolis Chains -- 3.10.2 Random Walk Metropolis Chains -- 3.10.3 Scaling of the Random Walk Metropolis -- 3.11 Metropolis Algorithms Illustrated with the Multinomial Logit Model -- 3.12 Hybrid MCMC Methods -- 3.13 Diagnostics -- Chapter 4 Unit‐Level Models and Discrete Demand -- 4.1 Latent Variable Models -- 4.2 Multinomial Probit Model -- 4.2.1 Understanding the Autocorrelation Properties of the MNP Gibbs Sampler -- 4.2.2 The Likelihood for the MNP Model -- 4.3 Multivariate Probit Model -- 4.4 Demand Theory and Models Involving Discrete Choice -- 4.4.1 A Nonhomothetic Choice Model -- 4.4.2 Demand for Discrete Quantities -- 4.4.3 Demand for Variety -- Chapter 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 -- 5.5.1 A Hybrid Sampler -- 5.5.2 Identification of the Number of Mixture Components -- 5.5.3 Application to Hierarchical Models -- 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 -- Chapter 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 from the Posterior -- 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 MNP models -- 6.10 Marketing Decisions and Bayesian Decision Theory -- 6.10.1 Plug‐In vs Full Bayes Approaches -- 6.10.2 Use of Alternative Information Sets -- 6.10.3 Valuation of Disaggregate Information -- 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information -- Chapter 7 Simultaneity -- 7.1 A Bayesian Approach to Instrumental Variables -- 7.2 Structural Models and Endogeneity/Simultaneity -- 7.2.1 Demand Model -- 7.2.2 Supply Model - Profit Maximizing Prices -- 7.2.3 Bayesian Estimation -- 7.3 Non‐Random Marketing Mix Variables -- 7.3.1 A General Framework -- 7.3.2 An Application to Detailing Allocation -- 7.3.3 Conditional Modeling Approach -- 7.3.4 Beyond the Conditional Model -- Chapter 8 A Bayesian Perspective on Machine Learning -- 8.1 Introduction -- 8.2 Regularization -- 8.2.1 The LASSO and Bayes -- 8.2.2 Discussion: Informative Regularizers -- 8.2.3 Bayesian Inference -- 8.3 Bagging -- 8.3.1 Bagging for Regression -- 8.3.2 Bagging, Bayesian Model Averaging and Ensembles -- 8.4 Boosting -- 8.4.1 Boosting as Bayes -- 8.5 Deep Learning -- 8.5.1 A Primer on Deep Learning -- 8.5.2 Bayes and Deep Learning -- 8.6 Applications -- 8.6.1 Bayes/ML for Flexible Heterogeneity -- 8.6.2 The Need for ML -- 8.6.3 Discussion -- Chapter 9 Bayesian Analysis for Text Data -- 9.1 Introduction -- 9.2 Consumer Demand -- 9.2.1 The Latent Dirichlet Allocation (LDA) Model -- 9.2.2 Full Gibbs Sampler -- 9.2.3 Processing Text Data for Analysis -- 9.2.4 Collapsed Gibbs Sampler -- 9.2.5 The Sentence Constrained LDA Model -- 9.2.6 Conjunctions and Punctuation -- 9.3 Integrated Models -- 9.3.1 Text and Conjoint Data -- 9.3.2 R Code for Text and Conjoint Data -- 9.3.3 Text and Product Ratings -- 9.3.4 Text and Scaled Response Data -- 9.4 Discussion. ; Chapter 10 Case Study 1: Analysis of Choice‐Based Conjoint Data Using A Hierarchical Logit Model -- 10.1 Choice‐Based Conjoint -- 10.2 A Random Coefficient Logit -- 10.3 Sign Constraints and Priors -- 10.4 The Camera Data -- 10.4.1 Panel Data in bayesm -- 10.5 Running the Model -- 10.6 Describing the Draws of Respondent Partworths -- 10.7 Predictive Posteriors -- 10.7.1 Respondent‐Level Parthworth Inferences -- 10.7.2 Posterior Predictive Distributions -- 10.8 COMPARISON OF STAN AND SAWTOOTH SOFTWARE TO BAYESM ROUTINES -- 10.8.1 Comparison to STAN -- 10.8.2 Comparison with Sawtooth Software -- Chapter 11 Case Study 2: WTP and Equilibrium Analysis with Conjoint Demand -- 11.1 The Demand for Product Features -- 11.1.1 The Standard Choice Model for Differentiated Product Demand -- 11.1.2 Estimating Demand -- 11.2 Conjoint Surveys and Demand Estimation -- 11.2.1 Conjoint Design -- 11.3 WTP Properly Defined -- 11.3.1 Pseudo‐WTP -- 11.3.2 Pseudo WTP for Heterogenous Consumers -- 11.3.3 True WTP -- 11.3.4 Problems with All WTP Measures -- 11.4 Nash Equilibrium Prices - Computation and Assumptions -- 11.4.1 Assumptions -- 11.4.2 A Standard Logit Model for Demand -- 11.4.3 Computing Equilibrium Prices -- 11.5 Camera Example -- 11.5.1 WTP Computations -- 11.5.2 Equilibrium Price Calculations -- 11.5.3 Lessons for Conjoint Design from WTP and Equilibrium Price Computations -- Chapter 12 Case Study 3: Scale Usage Heterogeneity -- 12.1 Background -- 12.2 Model -- 12.3 Priors and MCMC Algorithm -- 12.4 Data -- 12.4.1 Scale Usage Heterogeneity -- 12.4.2 Correlation Analysis -- 12.5 Discussion -- 12.6 R Implementation -- Chapter 13 Case Study 4: Volumetric Conjoint -- 13.1 Introduction -- 13.2 Model Development -- 13.3 Estimation -- 13.4 Empirical Analysis -- 13.4.1 Ice Cream -- 13.4.2 Frozen Pizza -- 13.5 Discussion -- 13.6 Using the Code -- 13.7 Concluding Remarks. ; Chapter 14 Case Study 5: Approximate Bayes and Personalized Pricing -- 14.1 Heterogeneity and Heterogeneous Treatment Effects -- 14.2 The Framework -- 14.2.1 Introducing the ML Element -- 14.3 Context and Data -- 14.4 Does the Bayesian Bootstrap Work? -- 14.5 A Bayesian Bootstrap Procedure for the HTE Logit -- 14.5.1 The Estimator -- 14.5.2 Results -- 14.6 Personalized Pricing -- A An Introduction to R and bayesm -- A.1 SETTING UP THE R ENVIRONMENT AND BAYESM -- A.1.1 Obtaining R -- A.1.2 Getting Started in RStudio -- A.1.3 Obtaining Help in RStudio -- A.1.4 Installing bayesm -- A.2 The R Language -- A.2.1 Using Built‐In Functions: Running a Regression -- A.2.2 Inspecting Objects and the R Workspace -- A.2.3 Vectors, Matrices, and Lists -- A.2.4 Accessing Elements and Subsetting Vectors, Arrays, and Lists -- A.2.5 Loops -- A.2.6 Implicit Loops -- A.2.7 Matrix Operations -- A.2.8 Other Useful Built‐In R Functions -- A.2.9 User‐defined Functions -- A.2.10 Debugging Functions -- A.2.11 Elementary Graphics -- A.2.12 System Information -- A.2.13 More Lessons Learned from Timing -- A.3 USING BAYESM -- A.4 OBTAINING HELP WITH BAYESM -- A.5 Tips on Using MCMC Methods -- A.6 Extending and Adapting Our Code -- References -- Index -- EULA.
• Sommario/riassunto: Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.
• Titolo autorizzato: Bayesian Statistics and Marketing
• ISBN: 1-394-21914-8; 1-394-21913-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911019737103321
• Lo trovi qui: Univ. Federico II
• Serie: WILEY SERIES in PROB and STATISTICS/see 1345/6,6214/5 Series