This paper makes the case that maximum likelihood is usually better than multiple imputation for handling missing data and demonstrates how maximum likelihood for missing data can readily be implemented with the following SAS procedures: MI, MIXED, GLIMMIX, CALIS and QLIM.
This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques. It presents SAS procedures PROC MI and PROC MIANALYZE for creating multiple imputations for incomplete multivariate data and for analyzing...
Durham, NC 27708 | 919.681.6019