Bayesian statistics and marketing /

Bayesian statistics and marketing /

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Bibliographic Details
Author / Creator:Rossi, Peter E. (Peter Eric), 1955-
Imprint:Chichester, West Sussex, England ; Hoboken, NJ : John Wiley, c2005.
Description:x, 348 p. : ill. ; 26 cm.
Language:English
Series:Wiley series in probability and statistics
Subject:
Marketing research -- Mathematical models.
Marketing -- Mathematical models.
Bayesian statistical decision theory.
Bayesian statistical decision theory.
Marketing -- Mathematical models.
Marketing research -- Mathematical models.
Format: E-Resource Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5820397
Hidden Bibliographic Details
Other authors / contributors:Allenby, Greg M. (Greg Martin), 1956-
McCulloch, Robert E. (Robert Edward)
ISBN:9780470863671 (acid-free paper)
0470863676 (acid-free paper)
Notes:Includes bibliographical references (p. [335]-339) and index.
Table of Contents:
  • 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. Computing and This Book
  • Acknowledgements
  • 2. Bayesian Essentials
  • 2.0. Essential Concepts from Distribution Theory
  • 2.1. The Goal of Inference and Bayes' Theorem
  • 2.2. Conditioning and the Likelihood Principle
  • 2.3. Prediction and Bayes
  • 2.4. Summarizing the Posterior
  • 2.5. Decision Theory, Risk, and the Sampling Properties of Bayes Estimators
  • 2.6. Identification and Bayesian Inference
  • 2.7. Conjugacy, Sufficiency, and Exponential Families
  • 2.8. Regression and Multivariate Analysis Examples
  • 2.9. Integration and Asymptotic Methods
  • 2.10. Importance Sampling
  • 2.11. Simulation Primer for Bayesian Problems
  • 2.12. Simulation from Posterior of Multivariate Regression Model
  • 3. Markov Chain Monte Carlo Methods
  • 3.1. Markov Chain Monte Carlo 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 Seemingly Unrelated 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
  • 3.10. Metropolis Algorithms
  • 3.11. Metropolis Algorithms Illustrated with the Multinomial Logit Model
  • 3.12. Hybrid Markov Chain Monte Carlo Methods
  • 3.13. Diagnostics
  • 4. Unit-Level Models and Discrete Demand
  • 4.1. Latent Variable Models
  • 4.2. Multinomial Probit Model
  • 4.3. Multivariate Probit Model
  • 4.4. Demand Theory and Models Involving Discrete Choice
  • 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
  • 5.6. 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
  • 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
  • 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 Multinomial Probit Models
  • 6.10. Marketing Decisions and Bayesian Decision Theory
  • 6.11. An Example of Bayesian Decision Theory: Valuing Household Purchase Information
  • 7. Simultaneity
  • 7.1. A Bayesian Approach to Instrumental Variables
  • 7.2. Structural Models and Endogeneity/Simultaneity
  • 7.3. Nonrandom Marketing Mix Variables
  • Case Study 1: A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts
  • Background
  • Model
  • Data
  • Results
  • Discussion
  • R Implementation
  • Case Study 2: Modeling Interdependent Consumer Preferences
  • Background
  • Model
  • Data
  • Results
  • Discussion
  • R Implementation
  • Case Study 3: Overcoming Scale Usage Heterogeneity
  • Background
  • Model
  • Priors and MCMC Algorithm
  • Data
  • Discussion
  • R Implementation
  • Case Study 4: A Choice Model with Conjunctive Screening Rules
  • Background
  • Model
  • Data
  • Results
  • Discussion
  • R Implementation
  • Case Study 5: Modeling Consumer Demand for Variety
  • Background
  • Model
  • Data
  • Results
  • Discussion
  • R Implementation
  • Appendix A. An Introduction to Hierarchical Bayes Modeling in R
  • A.1. Setting Up the R Environment
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