Missing Values in Linear Multiple Discriminant Analysis

This paper presents the results of an empirical study of handling the problem of missing values in a discriminant function analysis where both number of variables and number of individuals were very large. Elimination of individuals with missing values is considered and rejected. Estimation of missing values using means and estimation by an iterative regression technique are essayed, and the results compared. In this particular study, the far simpler method of using means for missing values gives comparable results with the regression estimation technique. Additional studies of the estimation techniques are recommended.