CHOOSING AMONG IMPUTATION TECHNIQUES FOR INCOMPLETE MULTIVARIATE DATA - A SIMULATION STUDY

A wide variety of strategies for coping with the problem of missing values, which frequently arises in multivariate data, have been proposed and tried over the years. One popular and important strategy is to estimate the missing values themselves in some way, usually achieved by imputation techniques. By means of Monte Carlo simulations, this paper investigates the relative performance of five deterministic imputation techniques using normal and non-normal data with several factors that may affect their efficiency. The imputation techniques are: mean substitution method (MSM), EM algorithm (EM), Dear's principal component method (DPC), general iterative principal component method (GIP) and singular value decomposition method (SVD). GIP is a refined, iterative version of DPC, developed to overcome certain problems with the latter. Although results indicate that no single imputation technique is best overall in all combinations of factors studied, MSM and DPC behave erratically; when the intercorrelation among the variables is moderate or high, they performed worse than the iterative imputation techniques-EM, SVD, and GIP-which, under this condition, are equally efficient. An illustrative real data example is given.