Open Access
August 2016 Goodness of fit tests in terms of local levels with special emphasis on higher criticism tests
Veronika Gontscharuk, Sandra Landwehr, Helmut Finner
Bernoulli 22(3): 1331-1363 (August 2016). DOI: 10.3150/14-BEJ694

Abstract

Instead of defining goodness of fit (GOF) tests in terms of their test statistics, we present an alternative method by introducing the concept of local levels, which indicate high or low local sensitivity of a test. Local levels can act as a starting point for the construction of new GOF tests. We study the behavior of local levels when applied to some well-known GOF tests such as Kolmogorov–Smirnov (KS) tests, higher criticism (HC) tests and tests based on phi-divergences. The main focus is on a rigorous characterization of the asymptotic behavior of local levels of the original HC tests which leads to several further asymptotic results for local levels of other GOF tests including GOF tests with equal local levels. While local levels of KS tests, which are related to the central range, are asymptotically strictly larger than zero, all local levels of HC tests converge to zero as the sample size increases. Consequently, there exists no asymptotic level $\alpha$ GOF test such that all local levels are asymptotically bounded away from zero. Finally, by means of numerical computations we compare classical KS and HC tests to a GOF test with equal local levels.

Citation

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Veronika Gontscharuk. Sandra Landwehr. Helmut Finner. "Goodness of fit tests in terms of local levels with special emphasis on higher criticism tests." Bernoulli 22 (3) 1331 - 1363, August 2016. https://doi.org/10.3150/14-BEJ694

Information

Received: 1 February 2014; Revised: 1 October 2014; Published: August 2016
First available in Project Euclid: 16 March 2016

zbMATH: 06579699
MathSciNet: MR3474818
Digital Object Identifier: 10.3150/14-BEJ694

Keywords: higher criticism statistic , Kolmogorov–Smirnov test , local levels , minimum $p$-value test , Normal and Poisson approximation , order statistics

Rights: Copyright © 2016 Bernoulli Society for Mathematical Statistics and Probability

Vol.22 • No. 3 • August 2016
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