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An ℓ∞ asymptotically nearly minimax goodness of fit test

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  • Klass, Michael J.

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.

Suggested Citation

  • Klass, Michael J., 2022. "An ℓ∞ asymptotically nearly minimax goodness of fit test," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 1238-1270.
    • Handle: RePEc:eee:spapps:v:150:y:2022:i:c:p:1238-1270
    DOI: 10.1016/j.spa.2022.03.011
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