Bootstrapping modified goodness-of-fit statistics with estimated parameters

Abstract

Goodness-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. (C) 2004 Elsevier B.V. All rights reserved.