Locally robust methods and near-parametric asymptotics
type: Item
instance of: scholarly article
title: Locally robust methods and near-parametric asymptotics
zbMATH Open document ID: 1407.62123
author: Kanta Naito
author: Spiridon Penev
published in: Journal of Multivariate Analysis
publication date: 2018-08-16T00:00:00Z
review text: It is known that the localization of likelihood based methods for regression and density estimation improve the resulting estimators with respect to suitable global risk measures. The authors show that a similar effect can also be observed with respect to robust estimation procedures. They prove that the localized versions of robust density estimation methods perform better with respect to global risk measures based on minimization of Bregman divergence measures.
reviewed by: B. L. S. Prakasa Rao
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62G35
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 6919563
zbMATH Keywords: Bregman divergence
zbMATH Keywords: kernel
zbMATH Keywords: power divergence
zbMATH Keywords: risk
zbMATH Keywords: robustness
describes a project that uses: KernSmooth
MaRDI profile type: Publication
full work available at URL: j.jmva.2018.06.006
OpenAlex ID: W2809043644
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Wikidata QID: Q129652931
DOI: 10.1016/J.JMVA.2018.06.006
description: scientific article; zbMATH DE number 6919563