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A Remark on Unified Error Exponents: Hypothesis Testing, Data Compression and Measure Concentration

Accepted version
Peer-reviewed

Type

Book chapter

Change log

Authors

Kontoyiannis, Ioannis  ORCID logo  https://orcid.org/0000-0001-7242-6375
Sezer, Ali Devin 

Abstract

Let A be finite set equipped with a probability distribution P, and let M be a “mass” function on A. A characterization is given for the most efficient way in which A n can be covered using spheres of a fixed radius. A covering is a subset C n of A n with the property that most of the elements of A n are within some fixed distance from at least one element of C n , and “most of the elements” means a set whose probability is exponentially close to one (with respect to the product distribution P n ). An efficient covering is one with small mass M n (C n ). With different choices for the geometry on A, this characterization gives various corollaries as special cases, including Marton’s error-exponents theorem in lossy data compression, Hoeffding’s optimal hypothesis testing exponents, and a new sharp converse to some measure concentration inequalities on discrete spaces.

Description

Title

A Remark on Unified Error Exponents: Hypothesis Testing, Data Compression and Measure Concentration

Keywords

math.PR, math.PR, math.OC

Is Part Of

Stochastic Inequalities and Applications

Book type

Publisher

Springer

ISBN

978-3-0348-9428-9