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"Peter Rossi, an expert on Bayesian analysis, presents a crisp introduction to an increasingly important class of models and their use in econometric applications."
--Andrew Gelman, Columbia University"This book shows that a combination of the Bayesian paradigm and (infinite) mixtures of normal distributions can be used to construct a very flexible and robust class of semi- or non-parametric methods. Rossi presents these methods in such a way that they can be applied by anyone with a basic knowledge of Bayesian econometrics. The book will be highly valued as a source of inspiration for incorporating non-parametric ideas in Bayesian models and as a reference for many applications of these techniques."
--Dennis Fok, Erasmus University Rotterdam"Rossi shows that the Bayesian approach to statistics can be applied to marketing and microeconometrics data without making the strong 'parametric' assumptions about functional forms and error distribution that are commonly made. The discussion and examples make a good case for the non-parametric Bayesian approach to these problems, and researchers will find it a valuable resource."
--Edward Greenberg, professor emeritus, Washington University in St. Louis
List of contents
About the author
Peter E. Rossi is the James Collins Professor of Marketing, Economics, and Statistics at UCLA's Anderson School of Management. He has published widely in marketing, economics, statistics, and econometrics and is a coauthor of
Bayesian Statistics and Marketing.
Summary
Reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. This book advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals.
Additional text
"As the creator of bayesm (R software for Bayesian inference) and lead author of Bayesian Statistics and Marketing, Rossi has deep knowledge of the book's titular methods."
