The Annals of Statistics

$\Gamma$-Minimax Selection Procedures in Simultaneous Testing Problems

Klaus J. Miescke

Full-text: Open access

Abstract

Suppose we have to decide on the basis of appropriately drawn samples which of $k$ treatment populations are "good" compared to either given control values or to a control population from which an additional sample is available. The unknown parameters are assumed to vary randomly according to a prior distribution about which we only have the partial knowledge that it is contained in a given class $\Gamma$ of priors. Though we derive in both cases (under the assumption of monotone likelihood ratios) $\Gamma$-minimax procedures which by definition attain minimal supremal risk over $\Gamma$, the emphases are different: while we try to demonstrate in the "known controls case" how well known results from the theory of testing hypotheses can be utilized to solve the problem, our main purpose in the "unknown control case" is to give a new proof for a theorem which was stated but only partially proved by Randles and Hollander.

Article information

Source
Ann. Statist., Volume 9, Number 1 (1981), 215-220.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345351

Digital Object Identifier
doi:10.1214/aos/1176345351

Mathematical Reviews number (MathSciNet)
MR600551

Zentralblatt MATH identifier
0455.62021

JSTOR
links.jstor.org

Subjects
Primary: 62F07: Ranking and selection
Secondary: 62F15: Bayesian inference

Keywords
Gamma minimax procedures simultaneous testing Bayesian procedures improper prior distributions

Citation

Miescke, Klaus J. $\Gamma$-Minimax Selection Procedures in Simultaneous Testing Problems. Ann. Statist. 9 (1981), no. 1, 215--220. doi:10.1214/aos/1176345351. https://projecteuclid.org/euclid.aos/1176345351


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