Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study

The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the parameters of the multiple linear regression. However, in the presence of outliers and when the model includes both continuous and categorical (factor) variables, the OLS can result in poor estimates. In...

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Main Authors:	A. Talib, Bashar, Midi, Habshah
Format:	Article
Language:	English
Published:	Universiti Putra Malaysia Press 2009
Online Access:	http://psasir.upm.edu.my/id/eprint/16589/ http://psasir.upm.edu.my/id/eprint/16589/ http://psasir.upm.edu.my/id/eprint/16589/1/16589.pdf

id	upm-16589
recordtype	eprints
spelling	upm-165892015-05-27T07:22:41Z http://psasir.upm.edu.my/id/eprint/16589/ Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study A. Talib, Bashar Midi, Habshah The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the parameters of the multiple linear regression. However, in the presence of outliers and when the model includes both continuous and categorical (factor) variables, the OLS can result in poor estimates. In this paper we try to introduce an alternative robust method for such a model that is much less influenced by the presence of outliers. A numerical example is presented to compare the performance of the OLS, the Re-weighted Least Squares based on the Robust Distance Least Absolute Value (RLSRDL1), and the Re-weighted Least Squares based on the Robust Distance S/M estimator (RLSRDSM). The latter is the modification of the RDL1. The empirical evidence shows that the performance of the RLSRDSM is fairly close to the RLSRDL1 up to 20% outliers. As the percentage of outliers increases to more than 20%, the RLSRDSM is slightly better than the RLSRDL1. However, the Robust Distance Least Absolute Value (RDL1) estimator posed certain computational problems such as degenerate non-unique solutions while the RLSRDSM do not have such problem. Universiti Putra Malaysia Press 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16589/1/16589.pdf A. Talib, Bashar and Midi, Habshah (2009) Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study. Malaysian Journal of Mathematical Sciences, 3 (2). pp. 161-181. ISSN 1823-8343 http://einspem.upm.edu.my/journal/volume3.2.php
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Putra Malaysia
building	UPM Institutional Repository
collection	Online Access
language	English
description	The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the parameters of the multiple linear regression. However, in the presence of outliers and when the model includes both continuous and categorical (factor) variables, the OLS can result in poor estimates. In this paper we try to introduce an alternative robust method for such a model that is much less influenced by the presence of outliers. A numerical example is presented to compare the performance of the OLS, the Re-weighted Least Squares based on the Robust Distance Least Absolute Value (RLSRDL1), and the Re-weighted Least Squares based on the Robust Distance S/M estimator (RLSRDSM). The latter is the modification of the RDL1. The empirical evidence shows that the performance of the RLSRDSM is fairly close to the RLSRDL1 up to 20% outliers. As the percentage of outliers increases to more than 20%, the RLSRDSM is slightly better than the RLSRDL1. However, the Robust Distance Least Absolute Value (RDL1) estimator posed certain computational problems such as degenerate non-unique solutions while the RLSRDSM do not have such problem.
format	Article
author	A. Talib, Bashar Midi, Habshah
spellingShingle	A. Talib, Bashar Midi, Habshah Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
author_facet	A. Talib, Bashar Midi, Habshah
author_sort	A. Talib, Bashar
title	Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
title_short	Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
title_full	Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
title_fullStr	Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
title_full_unstemmed	Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
title_sort	robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study
publisher	Universiti Putra Malaysia Press
publishDate	2009
url	http://psasir.upm.edu.my/id/eprint/16589/ http://psasir.upm.edu.my/id/eprint/16589/ http://psasir.upm.edu.my/id/eprint/16589/1/16589.pdf
first_indexed	2018-09-07T13:46:30Z
last_indexed	2018-09-07T13:46:30Z
_version_	1610956658958139392

Robust estimator to deal with regression models having both continuous and categorical regressors: a simulation study

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