Detection of influential observations in principle component regression

Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicolli...

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Bibliographic Details
Main Author:	Mokhtar Abdullah
Format:	Article
Published:	Universiti Kebangsaan Malaysia 1996
Online Access:	http://journalarticle.ukm.my/3686/ http://journalarticle.ukm.my/3686/

id	ukm-3686
recordtype	eprints
spelling	ukm-36862012-03-29T04:46:36Z http://journalarticle.ukm.my/3686/ Detection of influential observations in principle component regression Mokhtar Abdullah, Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicollinearity mayor may not be induced by the presence of influential observations. This paper discusses some diagnostic methods for identifying influential observations in the PCR. A data set on water quality of New York Rivers was considered to illustrate the methods. Universiti Kebangsaan Malaysia 1996-03 Article PeerReviewed Mokhtar Abdullah, (1996) Detection of influential observations in principle component regression. Sains Malaysiana, 25 (1). pp. 145-160. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol25num1_1996/vol25num1_96page145-160.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
description	Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicollinearity mayor may not be induced by the presence of influential observations. This paper discusses some diagnostic methods for identifying influential observations in the PCR. A data set on water quality of New York Rivers was considered to illustrate the methods.
format	Article
author	Mokhtar Abdullah,
spellingShingle	Mokhtar Abdullah, Detection of influential observations in principle component regression
author_facet	Mokhtar Abdullah,
author_sort	Mokhtar Abdullah,
title	Detection of influential observations in principle component regression
title_short	Detection of influential observations in principle component regression
title_full	Detection of influential observations in principle component regression
title_fullStr	Detection of influential observations in principle component regression
title_full_unstemmed	Detection of influential observations in principle component regression
title_sort	detection of influential observations in principle component regression
publisher	Universiti Kebangsaan Malaysia
publishDate	1996
url	http://journalarticle.ukm.my/3686/ http://journalarticle.ukm.my/3686/
first_indexed	2023-09-18T19:39:28Z
last_indexed	2023-09-18T19:39:28Z
_version_	1777405492491976704

Detection of influential observations in principle component regression

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