Functional delta-method for the bootstrap of quasi-Hadamard differentiable functionals
Abstract
The functional delta-method provides a convenient tool for deriving the asymptotic distribution of a plug-in estimator of a statistical functional from the asymptotic distribution of the respective empirical process. Moreover, it provides a tool to derive bootstrap consistency for plug-in estimators from bootstrap consistency of empirical processes. It has recently been shown that the range of applications of the functional delta-method for the asymptotic distribution can be considerably enlarged by employing the notion of quasi-Hadamard differentiability. Here we show in a general setting that this enlargement carries over to the bootstrap. That is, for quasi-Hadamard differentiable functionals bootstrap consistency of the plug-in estimator follows from bootstrap consistency of the respective empirical process. This enlargement often requires convergence in distribution of the bootstrapped empirical process w.r.t.\ a nonuniform sup-norm. The latter is not problematic as will be illustrated by means of examples.
Cite
@article{arxiv.1510.06207,
title = {Functional delta-method for the bootstrap of quasi-Hadamard differentiable functionals},
author = {Eric Beutner and Henryk Zähle},
journal= {arXiv preprint arXiv:1510.06207},
year = {2016}
}