Applications of intentionally biased bootstrap methods
type: Item
instance of: scholarly article
title: Applications of intentionally biased bootstrap methods
zbMATH Open document ID: 0898.62053
published in: Documenta Mathematica
publication date: 1998-08-05T00:00:00Z
full work available at URL: https://eudml.org/doc/231966
full work available at URL: Hall.MAN.html
review text: Summary: A class of weighted-bootstrap techniques, called biased-bootstrap methods, is proposed. It is motivated by the need to adjust more conventional, uniform-bootstrap methods in a surgical way, so as to alter some of their features while leaving others unchanged. Depending on the nature of the adjustment, the biased bootstrap can be used to reduce bias, or reduce variance, or render some characteristic equal to a predetermined quantity. More specifically, applications of bootstrap methods include hypothesis testing, variance stabilisation, both density estimation and nonparametric regression under constraints, `robustification' of general statistical procedures, sensitivity analysis, generalised method of moments, shrinkage, and many more.
Mathematics Subject Classification ID: 62G09
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62G05
zbMATH DE Number: 1184365
zbMATH Keywords: bias reduction
zbMATH Keywords: empirical likelihood
zbMATH Keywords: local-linear smoothing
zbMATH Keywords: curve estimation
zbMATH Keywords: variance stabilization
zbMATH Keywords: weighted-bootstrap
zbMATH Keywords: biased-bootstrap
author: Presnell, Brett
author: Peter Hall
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1184365