The Annals of Statistics

Empirical Bayes Estimation of a Distribution Function

Ramesh M. Korwar and Myles Hollander

Full-text: Open access

Abstract

A sequence of empirical Bayes estimators is defined for estimating a distribution function. The sequence is shown to be asymptotically optimal relative to a Ferguson Dirichlet process prior. Exact risk expressions are derived and the rate, at which the overall expected loss approaches the minimum Bayes risk, is exhibited. The empirical Bayes approach, based on the Dirichlet process, is also applied to the problem of estimating the mean of a distribution.

Article information

Source
Ann. Statist., Volume 4, Number 3 (1976), 581-588.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176343463

Digital Object Identifier
doi:10.1214/aos/1176343463

Mathematical Reviews number (MathSciNet)
MR413388

Zentralblatt MATH identifier
0358.62035

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62C99: None of the above, but in this section

Keywords
Distribution function empirical Bayes estimator Dirichlet process

Citation

Korwar, Ramesh M.; Hollander, Myles. Empirical Bayes Estimation of a Distribution Function. Ann. Statist. 4 (1976), no. 3, 581--588. doi:10.1214/aos/1176343463. https://projecteuclid.org/euclid.aos/1176343463


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