ENGLISH

Probability Matching Priors: Higher Order Asymptotics

By Gauri Sankar Datta , Rahul Mukerjee (auth.)

Book information

Publisher
Springer-Verlag New York
Year
2004
ISBN
978-0-387-20329-4, 978-1-4612-2036-7
DOI
10.1007/978-1-4612-2036-7
Language
english
Format
PDF
Filesize
48 MB (50644018 bytes)
Series
Lecture Notes in Statistics 178
Edition
1
Pages
127\137
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Statistical Theory and Methods; Probability Theory

Description

Probability matching priors, ensuring frequentist validity of posterior credible sets up to the desired order of asymptotics, are of substantial current interest. They can form the basis of an objective Bayesian analysis. In addition, they provide a route for obtaining accurate frequentist confidence sets, which are meaningful also to a Bayesian. This monograph presents, for the first time in book form, an up-to-date and comprehensive account of probability matching priors addressing the problems of both estimation and prediction. Apart from being useful to researchers, it can be the core of a one-semester graduate course in Bayesian asymptotics. Gauri Sankar Datta is a professor of statistics at the University of Georgia. He has published extensively in the fields of Bayesian analysis, likelihood inference, survey sampling, and multivariate analysis. Rahul Mukerjee is a professor of statistics at the Indian Institute of Management Calcutta. He co-authored three other research monographs, including A Calculus for Factorial Arrangements in this series. A fellow of the Institute of Mathematical Statistics, Dr. Mukerjee is on the editorial boards of several international journals.

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