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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Main Author: Mokhtar Abdullah,
Format: Article
Published: Universiti Kebangsaan Malaysia 1996
Online Access:http://journalarticle.ukm.my/3686/
http://www.ukm.my/jsm/english_journals/vol25num1_1996/vol25num1_96page145-160.html
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spelling my-ukm.journal.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
institution Universiti Kebangsaan Malaysia
building Perpustakaan Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
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://www.ukm.my/jsm/english_journals/vol25num1_1996/vol25num1_96page145-160.html
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score 13.19449