functionals of kernel-type estimators (1 < p < ∞) and is easy to implement in general, mainly due to its recourse to the bootstrap … method. The bootstrap procedure is based on nonparametric bootstrap applied to kernel-based test statistics, with estimated … "contact sets". We provide regularity conditions under which the bootstrap test is asymptotically valid uniformly over a large …

using the bootstrap. An empirical application illustrates the usefulness of shape restrictions for carrying out …

bootstrap algorithm to implement the proposed test and show its asymptotic validity. The proposed test procedure can apply to …

(g) can be carried out by using by using the bootstrap. An empirical application illustrates the usefulness of shape …

In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the … possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the … sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence …

distribution of a suitable estimator θ^n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically …(θ) and the bootstrap distribution of g(θ^n) coincide asymptotically. One implication is that Bayesians can interpret … bootstrap inference for g(θ) as approximately valid posterior inference in a large sample. Another implication−built on known …

estimators and establish the validity of a bootstrap procedure for estimating the limiting laws. Monte Carlo simulations support …