Handling Missing Data by Maximum Likelihood
Multiple imputation is rapidly becoming a popular method for handling missing data, especially with easy-to-use software like PROC MI. In this paper, however, I argue that maximum likelihood is usually better than multiple imputation for several important reasons. I then demonstrate how maximum likelihood for missing data can readily be implemented with the following SAS ® procedures: MI, MIXED, GLIMMIX, CALIS and QLIM.