Robust hypothesis testing with a relative entropy tolerance

Authors:
Bernard C. Levy
Affiliations:
Department of Electrical and Computer Engineering, University of California, Davis, CA
Venue:
IEEE Transactions on Information Theory
Year:
2009

Citing 3
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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.