  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.
