A Model-Based Approach to Handling Missing Values in S-PLUS using EM, Iterative Simulation, and Multiple Imputation

摘要

Missing data cripple most routines in statistical packages which typically expect a complete rectangular data set. The common practice is to artificially create a rectangular data set, usually by (1) throwing away cases with missing values or (2) ad hoc imputation, estimating and filling in missing data. These and other ad hoc approaches lose information and may give biased results.