The Treatment of Missing Values in Discriminant Analysis—I. The Sampling Experiment

Abstract

Abstract Probabilities of correct classification under several commonly used methods of handling missing values are studied by Monte Carlo methods. The methods include use of only complete observation vectors; use of all observations with no replacement; substitution of means for missing observations; Buck's regression method; and, Dear's principal component method. Discriminant functions were formed using independent random samples from two multivariate normal distributions with equal covariance matrices. Missing values occur randomly in each variable and independently of missing values in other variables. The mean substitution method and principal component method are, in general, superior to the other methods for cases considered.