Probability Matching Priors

Higher Order Asymptotics

Rahul Mukerjee, Gauri S. Datta - Collection Lecture Notes in Statistics

130 pages, parution le 13/02/2004

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Résumé

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.

Written for:
Researchers, Bayesian and frequentist; graduate students in Statistics

Sommaire

Introduction and the Shrinkage Argument
Matching Priors for Posterior Quantiles
Matching Priors for Distribution Functions
Matching Priors for Highest Posterior Density Regions
Matching Priors for Other Credible Regions
Matching Priors for Prediction

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	PAPIER
Éditeur(s)	Springer
Auteur(s)	Rahul Mukerjee, Gauri S. Datta
Collection	Lecture Notes in Statistics
Parution	13/02/2004
Nb. de pages	130
Format	15,5 x 23,5
Couverture	Broché
Poids	255g
Intérieur	Noir et Blanc
EAN13	9780387203294
ISBN13	978-0-387-20329-4