NDLI: Rules of application of goodness-of-fit tests in simple and composite hypothesis testing

Content Provider	IEEE Xplore Digital Library
Author	Lemeshko, B.Yu. Postovalov, S.N. Chimitova, E.V.
Copyright Year	2003
Description	Author affiliation: Novosibirsk State Tech. Univ., Russia (Lemeshko, B.Yu.; Postovalov, S.N.; Chimitova, E.V.)
Abstract	Problems of application of nonparametric goodness-of-fit tests and x/sup 2/ type tests have been considered. The following points concerning the tests of x/sup 2/ type have been considered: (a) correctness problems in usage of x/sup 2//sub k-r-1/ -distributions as the limiting distribution laws depending on estimation method used; (b) grouping methods providing maximal test power for close alternative hypotheses; (c) choice of the optimal interval number by the maximal test power. Optimal grouping tables have been constructed. Nonparametric tests of Kolmogorov type, /spl omega//sup 2/ and /spl Omega//sup 2/ Mises type lose their property of "independence from distribution" in composite hypothesis testing. The limiting distribution laws depend on the distribution corresponding to the hypothesis under test, number and type of estimated parameters, their values and the estimation method. The models of limiting distributions for nonparametric test statistics have been constructed for a number of composite hypotheses. Obtained results are included in the GOSSTANDART recommendations of Russia "Applied statistics. Rules of check of experimental and theoretical distribution of the consent". Part 1 Goodness-of-fit tests of a type chi-square (P 50.1.033-2001), part 2 Nonparametric goodness-of-fit tests (P 50.1.037-2002).
Starting Page	369
Ending Page	375
File Size	344651
Page Count	7
File Format	PDF
ISBN	8978686176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-28
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	7th Korea-Russia Intl Sym on Science
Subject Keyword	Testing Statistical distributions Statistical analysis Parameter estimation Maximum likelihood estimation Region 8 Probability distribution Random variables Statistics
Content Type	Text
Resource Type	Article

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