Probability Matching Priors
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Probability Matching Priors Higher Order Asymptotics
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Author : Gauri Sankar Datta
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Probability Matching Priors Higher Order Asymptotics written by Gauri Sankar Datta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Probability Matching Priors
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Author : Gauri Sankar Datta
language : en
Publisher:
Release Date : 2004-01-01
Probability Matching Priors written by Gauri Sankar Datta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with categories.
Probability Matching Priors For Non Regular Cases
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Author : Subhashis Ghosal
language : en
Publisher:
Release Date : 1998
Probability Matching Priors For Non Regular Cases written by Subhashis Ghosal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Probability Matching Priors For The Bivariate Normal Distribution
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Author : Upasana Santra
language : en
Publisher:
Release Date : 2008
Probability Matching Priors For The Bivariate Normal Distribution written by Upasana Santra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
There however, does not exist a prior that satisfies the matching via distribution functions criterion in this case. Finally, a general class of priors have been obtained for inference about the ratio of standard deviations. The propriety of the resultant posteriors is proved in each case under mild conditions and simulation results suggest that the approximations are valid even for moderate sample sizes. Further, several likelihood based methods have been considered for the correlation coefficient. One common feature of all these modified likelihoods is that they are all dependent on the data only through the sample correlation coefficient r.
Probability Matching Priors For An Extended Statistical Calibration Model
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Author : Daniel R. Eno
language : en
Publisher:
Release Date : 1999
Probability Matching Priors For An Extended Statistical Calibration Model written by Daniel R. Eno and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Calibration categories.
Bayesian Tolerance Intervals With Probability Matching Priors
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Author : Dharini Pathmanathan
language : en
Publisher:
Release Date : 2014
Bayesian Tolerance Intervals With Probability Matching Priors written by Dharini Pathmanathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Bayesian statistical decision theory categories.
Empirical Bayes And Likelihood Inference
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Author : S.E. Ahmed
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Empirical Bayes And Likelihood Inference written by S.E. Ahmed and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Bayesian and such approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both emphasize the construction of interval estimates of unknown parameters. In this volume, researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for research in the theory of inference.
Objective Bayes And Conditional Frequentist Inference
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Author :
language : en
Publisher:
Release Date : 2011
Objective Bayes And Conditional Frequentist Inference written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Noninformative Priors For Some Models Useful In Reliability And Survival Analysis
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Author : Gunhee Lee
language : en
Publisher:
Release Date : 1996
Noninformative Priors For Some Models Useful In Reliability And Survival Analysis written by Gunhee Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Distribution (Probability theory) categories.
The development of noninformative priors in the stress-strength model has an important bearing in reliability and survival analysis when vague initial states of knowledge are presented. It is known that the commonly used Jeffreys prior is not desirable in many multiparameter cases. Two recent approaches for finding noninformative priors are introduced. The first one is the Berger and Bernardo reference prior algorithm that maximizes the expected Kullback-Liebler divergence between posterior and prior density. The second one is the matching prior identified by matching the asymptotic frequentist coverage probability of one sided posterior credible interval with the desired confidence level. Therefore, by using the optimal noninformative prior, a Bayesian credible interval also corresponds to an accurate frequentist confidence interval. The Berger and Bernardo reference priors are derived for different ordered groups when the parameters of interest are reliabilities in several stress-strength models. By solving Datta and Ghosh's partial differential equations, matching priors are also derived for each stress-strength model. It is shown that the Berger and Bernardo reference prior is a matching prior in some models. Small sample comparisons using the Berger and Bernardo reference prior, the matching prior and the Jeffreys prior are done via Monte-Carlo simulation for a number of cases. The simulation study shows that the matching prior is an important approach to match the desired frequentist coverage probability even for small sample size cases.
Objective Bayesian Inference
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Author : James O Berger
language : en
Publisher: World Scientific
Release Date : 2024-03-06
Objective Bayesian Inference written by James O Berger and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-06 with Mathematics categories.
Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis of data.A widespread misconception is that Bayesian analysis is a more subjective theory of statistical inference than what is now called classical statistics. This is true neither historically nor in practice. Indeed, objective Bayesian analysis dominated the statistical landscape from roughly 1780 to 1930, long before 'classical' statistics or subjective Bayesian analysis were developed. It has been a subject of intense interest to a multitude of statisticians, mathematicians, philosophers, and scientists. The book, while primarily focusing on the latest and most prominent objective Bayesian methodology, does present much of this fascinating history.The book is written for four different audiences. First, it provides an introduction to objective Bayesian inference for non-statisticians; no previous exposure to Bayesian analysis is needed. Second, the book provides an overview of the development and current state of objective Bayesian analysis and its relationship to other statistical approaches, for those with interest in the philosophy of learning from data. Third, the book presents a careful development of the particular objective Bayesian approach that we recommend, the reference prior approach. Finally, the book presents as much practical objective Bayesian methodology as possible for statisticians and scientists primarily interested in practical applications.