title: Logistic regression when binary predictor variables are highly correlated.
abstract: Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.
authors.affiliation: National Immunization Program, Centers for Disease Control and Prevention, 1600 Clifton Ave, MS E62, Atlanta, GA 30033, USA. isb8@cdc.gov
authors.forename: L
authors.initials: L
authors.lastname: Barker
authors.forename: C
authors.initials: C
authors.lastname: Brown
mesh_terms: D003198:Computer Simulation; D016015:Logistic Models; D008954:Models, Biological