Robust Hypothesis Testing with a Relative Entropy Tolerance

Bernard C. Levy

doi:10.1109/tit.2008.2008128

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arXiv · 0707.2926

Robust Hypothesis Testing with a Relative Entropy Tolerance

Bernard C. Levy

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Abstract

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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Bernard C. Levy. 2008-04-02. Robust Hypothesis Testing with a Relative Entropy Tolerance. https://doi.org/10.1109/tit.2008.2008128

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