Robust Hypothesis Testing with a Relative Entropy Tolerance

Details Robust Hypothesis Testing with a Relative Entropy Tolerance Robust Hypothesis Testing with a Relative Entropy Tolerance

2007-07-19

arxiv.org/abs/0707.2926v2

Computer Science

Information Theory

14 pages, 5 figures, submitted to the IEEE Transactions on Information Theory, July 2007, revised April 2008

Scientific paper

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 illustrated by considering the transmission of binary data in the presence of additive noise.

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