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Gibbs Base Random Partitions Read chapter Read first chapter

2020 | OriginalPaper | Chapter

6. A Characterization of Jeffreys’ Prior with Its Implications to Likelihood Inference

Authors : Takemi Yanagimoto, Toshio Ohnishi

Published in: Pioneering Works on Distribution Theory

Publisher: Springer Singapore

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Abstract

A characterization of Jeffreys’ prior for a parameter of a distribution in the exponential family is given by the asymptotic equivalence of the posterior mean of the canonical parameter to the maximum likelihood estimator. A promising role of the posterior mean is discussed because of its optimality property. Further, methods for improving estimators are explored, when neither the posterior mean nor the maximum likelihood estimator performs favorably. The possible advantages of conjugate analysis based on a suitably chosen prior are examined.

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previous chapter Properties of General Systems of Orthogonal Polynomials with a Symmetric Matrix Argument
Appendix
Available only for authorised users
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Metadata
Title
A Characterization of Jeffreys’ Prior with Its Implications to Likelihood Inference
Authors
Takemi Yanagimoto
Toshio Ohnishi
Copyright Year
2020
Publisher
Springer Singapore
Book
Pioneering Works on Distribution Theory

Print ISBN: 978-981-15-9662-9

Electronic ISBN: 978-981-15-9663-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-9663-6_6

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