LA COLINEALIDAD Y LA SEPARACIÓN EN LOS DATOS EN EL MODELO DE REGRESIÓN LOGÍSTICA.

Collinearity and the lack of overlap in the data are problems that affect inference based on the logistic regression model. Simulation was used to investigate how the estimators that deal with collinearity (iterative Ridge) are affected, along with separation in the data (Firth's, and Rousseeuw and Christmann's) or both problems (Shen and Gao's). These estimators were compared considering the scaled condition number of the estimated information matrix, the bias and the mean squared error. In each one of the four scenarios studied, formed by using two levels of collinearity and two sample sizes, three degrees of overlap were considered in the data. It was found that iterative Ridge and Shen and Gao's estimators have null conditioning, as well as smaller bias and mean square error. The degree of overlap and the level of collinearity strongly affect the bias and mean square error of the maximum likelihood, Firth's and Rousseeuw and Christmann's estimators. [ABSTRACT FROM AUTHOR]