Robust Hypothesis Testing With a Relative Entropy Tolerance

被引：37
|
作者：
Levy, Bernard C. [1 ]
机构：
[1] Univ Calif Davis, Dept Elect & Comp Engn, Davis, CA 95616 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2009年 / 55卷 / 01期
关键词：
Kullback-Leibler divergence; least favorable densities; min-max problem; robust hypothesis testing; saddle point; BINARY-INPUT CHANNELS; NOISE;
D O I：
10.1109/TIT.2008.2008128
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities. The minimax problem admits a saddle point which is characterized. The robust test applies a nonlinear transformation which flattens the nominal likelihood ratio in the vicinity of one. Results are illttstrated by considering the transmission of binary data in the presence of additive noise.
引用
收藏
页码：413 / 421
页数：9
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