A simulation study of imputation techniques in linear quadratic and kernel discriminant analyses

Abstract

The problem of incomplete training data crops up fairly frequently in discriminant analysis. A time-honoured solution is to impute (fill in) data to the missing values. Working under the assumption that missing values are missing at random (MAR), this paper examines the effects of imputed values on the classification performance of linear (LDF)-, quadratic (QDF)-, and kernel (KDF)-discriminant functions. Five deterministic imputation techniques are studied and the problem of correctly implementing these techniques is addressed using normal and non-normal data. Monte Carlo results indicate that imputed values do not affect the underlying assumptions of discriminant analyses and, as the proportion of missing values increases, the efficiency of imputation techniques depends on the implementation procedure used.