PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A new lower bound for multiple hypothesis testing
Lucien Birgé
IEEE Information Theory 2005.

Abstract

The purpose of this paper is to give a new, easily tractable and sharp lower bound for the maximal error in multiple hypothesis testing with an application to nonasymptotic lower bounds for the minimax risk of estimators.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:694
Deposited By:Lucien Birgé
Deposited On:29 December 2004