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. The good news is that the last twenty five years have seen a revolution in methods for Handling Missing data . The new methods have much better statistical properties than traditional methods, while at the same time relying on weaker assumptions.

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

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  Data, Maximum, Handling, Missing, Likelihood, Handling missing data by maximum likelihood, Handling missing data

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