Probability Matching Priors: Higher Order Asymptotics (Lecture Notes in Statistics, 178)
Language: English
Published by Springer, 2004
- Softcover
- New
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- Title
- Probability Matching Priors: Higher Order Asymptotics (Lecture Notes in Statistics, 178)
- Author
- Datta, Gauri Sankar; Mukerjee, Rahul
- Publisher
- Springer
- Publication year
- 2004
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 038720329X
- ISBN 13
- 9780387203294
- Item weight
- 333 grams
- Series
- Book 25 of 72: Lecture Notes in Statistics
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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