Multivariate goodness-of-fit tests based on kernel density estimators
Aleksej Bakshaev
Vilnius University
Rimantas Rudzkis
Vilnius University
Published 2015-10-27


kernel density estimator

How to Cite

Bakshaev, A. and Rudzkis, R. (trans.) (2015) “Multivariate goodness-of-fit tests based on kernel density estimators”, Nonlinear Analysis: Modelling and Control, 20(4), pp. 585–602. doi:10.15388/NA.2015.4.9.


The paper is devoted to multivariate goodness-of-fit ests based on kernel density estimators. Both simple and composite null hypotheses are investigated. The test statistic is considered in the form of maximum of the normalized deviation of the estimate from its expected value. The produced comparative Monte Carlo power study shows that the proposed test is a powerful competitor to the existing classical criteria for testing goodness of fit against a specific type of an alternative hypothesis. An analytical way to establish the asymptotic distribution of the test statistic is discussed, using the approximation results for the probabilities of high excursions of differentiable Gaussian random fields.


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