A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns

摘要

An extensive investigation via simulation is carried out with the aim of comparing three nonparametric, single imputation methods in the presence of multiple data patterns. The ultimate goal is to provide useful hints for users needing to quickly pick the most effective imputation method among the following: Forward Imputation (Forlmp), considered in the two variants of with the principal component analysis (PCA), which alternates the use of PCA and the Nearest-Neighbour Imputation (NNI) method in a forward, sequential procedure, and with the Mahalanobis distance, which involves the use of the Mahalanobis distance when performing NNI; the iterative PCA technique, which imputes missing values simultaneously via PCA; the method, which is based on random forests and is developed for mixed-type data. The performance of these methods is compared under several data patterns characterized by different levels of kurtosis or skewness and correlation structures.