An effective selection of regression variables when the error distribution is incorrectly specified
type: Item
instance of: scholarly article
title: An effective selection of regression variables when the error distribution is incorrectly specified
zbMATH Open document ID: 0641.62043
DOI: 10.1007/BF02491488
published in: Annals of the Institute of Statistical Mathematics
publication date: 1987-01-01T00:00:00Z
review text: An asymptotically efficient selection of regression variables is considered in the situation where the statistician estimates regression parameters by the maximum likelihood method but fails to choose a likelihood function matching the true error distribution. The proposed procedure is useful when a robust regression technique is applied but the data in fact do not require that treatment. Examples and a Monte Carlo study are presented and relationships to other selectors such as Mallow's \(C_ p\) are investigated.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62F35
zbMATH DE Number: 4044969
zbMATH Keywords: model choice
zbMATH Keywords: Mallows Cp-criterion
zbMATH Keywords: asymptotically efficient selection of regression variables
zbMATH Keywords: maximum likelihood
zbMATH Keywords: robust regression
zbMATH Keywords: Monte Carlo study
author: Wolfgang K. Härdle
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf02491488
OpenAlex ID: W2103375873
description: scientific article; zbMATH DE number 4044969