Abstract
In this paper an essentially optimal asymptotically minimax goodness of fit test is introduced, especially useful for signal detection. In contrast to the chi-square goodness of fit tests, which are designed to detect the presence of an accumulation of small departures/deviations from the null distribution, this test is designed and succeeds at detecting the presence of a significant, substantial, local departure from the null distribution and therefore it is more powerful than chi-square tests for real world signal detection.
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