Open Access
September, 1989 Optimal-Partitioning Inequalities in Classification and Multi-Hypotheses Testing
Theodore P. Hill, Y. L. Tong
Ann. Statist. 17(3): 1325-1334 (September, 1989). DOI: 10.1214/aos/1176347272

Abstract

Optimal-partitioning and minimax risk inequalities are obtained for the classification and multi-hypotheses testing problems. Best possible bounds are derived for the minimax risk for location parameter families, based on the tail concentrations and Levy concentrations of the distributions. Special attention is given to continuous distributions with the maximum likelihood ratio property and to symmetric unimodal continuous distributions. Bounds for general (including discontinuous) distributions are also obtained.

Citation

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Theodore P. Hill. Y. L. Tong. "Optimal-Partitioning Inequalities in Classification and Multi-Hypotheses Testing." Ann. Statist. 17 (3) 1325 - 1334, September, 1989. https://doi.org/10.1214/aos/1176347272

Information

Published: September, 1989
First available in Project Euclid: 12 April 2007

zbMATH: 0683.62036
MathSciNet: MR1015154
Digital Object Identifier: 10.1214/aos/1176347272

Subjects:
Primary: 60E15
Secondary: 28B05 , 62H30

Keywords: classification and discriminant analysis , concentration function , convexity theorem , minimax risk , multi-hypotheses testing , Optimal-partitioning inequalities , tail concentration

Rights: Copyright © 1989 Institute of Mathematical Statistics

Vol.17 • No. 3 • September, 1989
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