Detection of influential observations in principle component regression

Mokhtar Abdullah, (1996) Detection of influential observations in principle component regression. Sains Malaysiana, 25 (1). pp. 145-160. ISSN 0126-6039

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Abstract

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.

Item Type:Article
Keywords:Multicollinearity;Principal component regression
Journal:Sains Malaysiana
ID Code:3686
Deposited By: Mr Fazli Nafiah -
Deposited On:15 Mar 2012 06:58
Last Modified:29 Mar 2012 04:46

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