Bayesian Non- and Semi-parametric Methods and Applications

Peter Rossi

ISBN 10: 0691145326 / ISBN 13: 9780691145327

Published by Princeton University Press, 2014

About this Item

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. Series: The Econometric and Tinbergen Institutes Lectures. Num Pages: 224 pages, 66 line illus. BIC Classification: PBTB. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 151 x 217 x 22. Weight in Grams: 412. . 2014. Hardcover. . . . .

Seller Inventory # V9780691145327

Bibliographic Details

• Title: Bayesian Non- and Semi-parametric Methods and Applications
• Publisher: Princeton University Press
• Publication Date: 2014
• Language: English
• ISBN 10: 0691145326
• ISBN 13: 9780691145327
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.

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.