An ℓ∞ asymptotically nearly minimax goodness of fit test

Michael J. Klass

Stochastic Processes and their Applications, 2022, vol. 150, issue C, 1238-1270

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.

Keywords: Hypothesis testing; Goodness of fit; ℓ∞-test; ℓ∞-goodness of fit test; Asymptotic minimax error; Departure and Bin parameters (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spa.2022.03.011

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