Premium
The Treatment of Missing Values in Logistic Regression
Author(s) -
Fung Karen Yuen,
Wrobel Barbara A.
Publication year - 1989
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710310106
Subject(s) - statistics , mathematics , logistic regression , discriminant function analysis , missing data , estimator , multivariate statistics , linear discriminant analysis , regression analysis , regression , linear regression
The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.