|
Showing 1 - 2 of
2 matches in All Departments
This third volume of case studies presents detailed applications of
Bayesian statistical analysis, emphasising the scientific context.
The papers were presented and discussed at a workshop held at
Carnegie-Mellon University, and this volume - dedicated to the
memory of Morrie Groot-reproduces six invited papers, each with
accompanying invited discussion, and nine contributed papers with
the focus on econometric applications.
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.
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.