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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| Format: | Article |
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Universiti Kebangsaan Malaysia
1996
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| Online Access: | http://journalarticle.ukm.my/3686/ http://www.ukm.my/jsm/english_journals/vol25num1_1996/vol25num1_96page145-160.html |
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| _version_ | 1831354943014961152 |
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| author | Mokhtar Abdullah, |
| author_facet | Mokhtar Abdullah, |
| author_sort | Mokhtar Abdullah, |
| building | Tun Sri Lanang Library |
| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my-ukm.journal-3686 |
| institution | Universiti Kebangsaan Malaysia |
| publishDate | 1996 |
| publisher | Universiti Kebangsaan Malaysia |
| record_format | eprints |
| 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 |
| spellingShingle | Mokhtar Abdullah, Detection of influential observations in principle component regression |
| title | 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_short | Detection of influential observations in principle component regression |
| title_sort | detection of influential observations in principle component regression |
| url | http://journalarticle.ukm.my/3686/ http://www.ukm.my/jsm/english_journals/vol25num1_1996/vol25num1_96page145-160.html |
| url_provider | http://journalarticle.ukm.my/ |