000			02493 a2200253 4500
003			OSt
005			20250224164542.0
008			250218b xxu||||| |||| 00| 0 eng d
020			_a9780691145327
040			_cIIT Kanpur
041			_aeng
082			_a330.01519542 _bR735b
100			_aRossi, Peter E.
245			_aBayesian non- and semi-parametric methods and applications _cPeter E. Rossi
260			_bPrinceton University Press _c2014 _aPrinceton
300			_axiii, 202p
440			_aThe econometric and tinbergen institutes lectures
490			_a/ edited by Herman K. van Dijk and Philip Hans Franses
520			_aThis 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.
650			_aBayesian statistical decision theory
650			_aEconometrics
650			_aEconomics -- Mathematical
942			_cBK
999			_c567369 _d567369