Academic Journal

Confidence distribution, the frequentist distribution estimator of a parameter — a Review. Int. Statist. Rev. page In press (Invited review article with discussions

Bibliographic Details
Title: Confidence distribution, the frequentist distribution estimator of a parameter — a Review. Int. Statist. Rev. page In press (Invited review article with discussions
Authors: Min-Ge Xie, Kesar Singh
Contributors: The Pennsylvania State University CiteSeerX Archives
Superior Title: http://homepages.math.uic.edu/%7Eminyang/Big%20Data%20Discussion%20Group/XieSingh_isr2013.pdf.
Publication Year: 2012
Collection: CiteSeerX
Description: Summary In frequentist inference, we commonly use a single point (point estimator) or an interval (confidence interval/"interval estimator") to estimate a parameter of interest. A very simple question is: Can we also use a distribution function ("distribution estimator") to estimate a parameter of interest in frequentist inference in the style of a Bayesian posterior? The answer is affirmative, and confidence distribution is a natural choice of such a "distribution estimator". The concept of a confidence distribution has a long history, and its interpretation has long been fused with fiducial inference. Historically, it has been misconstrued as a fiducial concept, and has not been fully developed in the frequentist framework. In recent years, confidence distribution has attracted a surge of renewed attention, and several developments have highlighted its promising potential as an effective inferential tool. This article reviews recent developments of confidence distributions, along with a modern definition and interpretation of the concept. It includes distributional inference based on confidence distributions and its extensions, optimality issues and their applications. Based on the new developments, the concept of a confidence distribution subsumes and unifies a wide range of examples, from regular parametric (fiducial distribution) examples to bootstrap distributions, significance (p-value) functions, normalized likelihood functions, and, in some cases, Bayesian priors and posteriors. The discussion is entirely within the school of frequentist inference, with emphasis on applications providing useful statistical inference tools for problems where frequentist methods with good properties were previously unavailable or could not be easily obtained. Although it also draws attention to some of the differences and similarities among frequentist, fiducial and Bayesian approaches, the review is not intended to re-open the philosophical debate that has lasted more than two hundred years. On the contrary, it is hoped ...
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1041.1641; http://homepages.math.uic.edu/%7Eminyang/Big%20Data%20Discussion%20Group/XieSingh_isr2013.pdf
Availability: http://homepages.math.uic.edu/%7Eminyang/Big%20Data%20Discussion%20Group/XieSingh_isr2013.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.DC239B3C
Database: BASE
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