Bootstrapping weighted empirical processes that do not converge weakly

Abstract

We show that the bootstrap method provides valid approximations to the sampling distribution of a weighted empirical process on D[0,1] even in the cases where it fails to converge weakly. Furthermore, the result is applied to construct valid bootstrap confidence sets in such pathological cases.Weighted empirical process Bootstrap Weak convergence Confidence sets