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A nonparametric hypothesis test via the Bootstrap resampling

By Tugrul Temel


This paper adapts an already existing nonparametric hypothesis test to the bootstrap framework. The test utilizes the nonparametric kernel regression method to estimate a measure of distance between the models stated under the null hypothesis. The bootstraped version of the test allows to approximate errors involved in the asymptotic hypothesis test. The paper also develops a Mathematica Code for the test algorithm.

Topics: C14 - Semiparametric and Nonparametric Methods: General, C12 - Hypothesis Testing: General, C15 - Statistical Simulation Methods: General
Year: 2011
OAI identifier: oai:mpra.ub.uni-muenchen.de:31880

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