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Hypothesis testing for small-sample scenarios is a practically importantproblem. In this paper, we investigate the robust hypothesis testing problem ina data-driven manner, where we seek the worst-case detector over distributionaluncertainty sets centered around the empirical distribution from samples usingSinkhorn distance. Compared with the Wasserstein robust test, the correspondingleast favorable distributions are supported beyond the training samples, whichprovides a more flexible detector. Various numerical experiments are conductedon both synthetic and real datasets to validate the competitive performances ofour proposed method.

A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets