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Principal Components Regression in Logistic Model
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 Title & Authors
Principal Components Regression in Logistic Model
Kim, Bu-Yong; Kahng, Myung-Wook;
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The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.
Customer relationship management;credit risk management;multicollinearity;logistic principal components regression;conjoint analysis;
 Cited by
로버스트추정에 바탕을 둔 주성분로지스틱회귀,김부용;강명욱;장혜원;

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