Transcription of 312-2012: Handling Missing Data by Maximum Likelihood
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312-2012: Handling Missing Data by Maximum Likelihood
1 Paper 312-2012 Handling Missing data by Maximum Likelihood Paul D. Allison, Statistical Horizons, Haverford, PA, USA ABSTRACT 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. INTRODUCTION Perhaps the most universal dilemma in statistics is what to do about Missing data . Virtually every data set of at least moderate size has some Missing data , usually enough to cause serious concern about what methods should be used.
Many traditional missing data techniques are valid only if the MCAR assumption holds. A considerably weaker (but still strong) assumption is that data are missing at random (MAR). Again, this is most easily defined in the case where only a single variable Y has missing data, and another set of variables X has no missing data.
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