Bayesian Statistics and Marketing

Name: Bayesian Statistics and Marketing
Brand: Wiley
Price: 91.99 EUR
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Peter E. Rossi Greg M. Allenby Sanjog Misra(Autor*in)

Wiley (Verlag)

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Erschienen am 8. Juli 2024

400 Seiten

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Beschreibung

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.

Peter Rossi is James Collins Distinguished University Professor of Marketing, Economics, and Statistics at the Anderson School of Management, UCLA, USA. He is the author of the popular R package, bayesm, and he has researched and published extensively on pricing and promotion, target marketing, and other related subjects.

Greg Allenby is Helen C. Kurtz Professor of Marketing as well as Professor of Statistics at the Fisher College of Business, Ohio State University, USA. He is a Fellow of the Informs Society for Marketing Science and the American Statistical Association, and he has published widely on the development and application of quantitative methods in marketing.

Sanjog Misra is Charles H. Kellstadt Professor of Marketing in the Booth School of Business, University of Chicago, USA. He has served as the co-editor of numerous high-impact journals, including Quantiative Marketing and Economics, and his research focuses on the use of machine learning and deep learning to study consumer and firm decisions.

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Inhalt

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

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