Robust variable selection for finite mixture regression models
type: Item
instance of: scholarly article
title: Robust variable selection for finite mixture regression models
zbMATH Open document ID: 1408.62050
author: Qingguo Tang
author: Rohana J. Karunamuni
published in: Annals of the Institute of Statistical Mathematics
publication date: 2018-05-29T00:00:00Z
review text: The problem of simultaneous estimation and variable selection in finite mixture models is considered. Specifically, a procedure based on minimum-distance techniques (namely, squared Hellinger distance) is proposed. The proposed estimator has the variable selection consistency and oracle property. Moreover, the global robustness properties of the proposed estimation are established by finding its finite-sample breakdown point. A Monte Carlo study is also performed together with a real data application.
reviewed by: Fabrizio Durante
Mathematics Subject Classification ID: 62F35
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 62J07
zbMATH DE Number: 6876224
zbMATH Keywords: finite mixture regression models
zbMATH Keywords: variable selection
zbMATH Keywords: minimum-distance methods
zbMATH Keywords: global robustness
describes a project that uses: flexmix
MaRDI profile type: Publication
full work available at URL: s10463-017-0602-4
OpenAlex ID: W2589445570
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DOI: 10.1007/S10463-017-0602-4
description: scientific article; zbMATH DE number 6876224