Bootstrapping modified goodness-of-fit statistics with estimated parameters
AbstractGoodness-of-fit tests are proposed for testing a composite null hypothesis that is a general parametric family of distribution functions. They are distribution-free under the null hypothesis and have a limiting normal distribution under the null and the alternative hypothesis. To avoid the estimation of the asymptotic variance under the alternative hypothesis, we propose consistent bootstrap estimators.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 71 (2005)
Issue (Month): 2 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Too, Yeu-Hua & Lin, Gwo Dong, 1989. "Characterizations of uniform and exponential distributions," Statistics & Probability Letters, Elsevier, vol. 7(5), pages 357-359, April.
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