Overview
- The first book on the topic of probability matching priors
Part of the book series: Lecture Notes in Statistics (LNS, volume 178)
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Table of contents (6 chapters)
Keywords
About this book
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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Bibliographic Information
Book Title: Probability Matching Priors: Higher Order Asymptotics
Authors: Gauri Sankar Datta, Rahul Mukerjee
Series Title: Lecture Notes in Statistics
DOI: https://doi.org/10.1007/978-1-4612-2036-7
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag New York, Inc. 2004
Softcover ISBN: 978-0-387-20329-4Published: 08 January 2004
eBook ISBN: 978-1-4612-2036-7Published: 06 December 2012
Series ISSN: 0930-0325
Series E-ISSN: 2197-7186
Edition Number: 1
Number of Pages: X, 127
Number of Illustrations: 2 b/w illustrations
Topics: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Econometrics