Abstract #300669


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JSM 2002 Abstract #300669
Activity Number: 212
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section*
Abstract - #300669
Title: Detecting Multicollinearity in Logistic Regression Models
Author(s): Ariful Haque*+ and Abbas Jawad and Mayadah Shabbout
Affiliation(s): Children's Hospital, Philadelphia and Children's Hospital, Philadelphia and Children's Hospital, Philadelphia
Address: 3535 Market Street, Suite 1032, Philadelphia, Pennsylvania, 19104, USA
Keywords: Multicollinearity ; Singular Value Decomposition ; Condition Index ; Variance Decomposition Proportions
Abstract:

This paper deals with the examination and detection of multicollinearity in logistic regression models. Belsley, Kuh, and Welsch (1980) suggested the diagnostic measures of condition indices and variance decomposition proportions for detecting multicollinearity in multiple linear regression. Here, we generalize these measures for detecting possible dependencies among the covariates in logistic regression models. In this paper, we show that our generalization is capable of detecting collinearity for logistic models by assessing the extent to which each of the logistic regression parameters is degraded by the presence of such collinear relations. This approach can be used for other type of generalized linear models since logistic regression is a class of generalized linear models. We illustrate the methodology with pediatric clinical data.


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