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Record Nr. |
UNINA9910877211403321 |
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Autore |
Rossi Peter E |
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Titolo |
Bayesian Statistics and Marketing |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2024 |
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©2024 |
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ISBN |
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1-394-21914-8 |
1-394-21913-X |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (402 pages) |
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Collana |
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WILEY SERIES in PROB and STATISTICS/see 1345/6,6214/5 Series |
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Altri autori (Persone) |
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AllenbyGreg M |
MisraSanjog |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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 |
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-- 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. |
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