Missing Data - EM Algorithm and Multiple Imputation.
If your rate of missing data is very, very small, it honestly doesn’t matter what technique you use. I’m talking very, very, very small (2-3%). There is another, better method for imputing single values, however, that is only slightly more difficult than mean imputation. It uses the E-M Algorithm, which stands for Expectation-Maximization.Missing data have important implications for analysis. At the very least, there is a loss of information and reduction in precision of inference on the population of interest relative to that intended.The EM algorithm heavily relies on the interpretation of observations as incomplete data but it does not have any control on the uncertainty of missing data. To effec-tively reduce the uncertainty of missing data, we present a regularized EM algorithm that penalizes the likelihood with the mutual information between the missing data.
A Review of Methods for Missing Data Therese D. Pigott Loyola University Chicago, Wilmette, IL, USA. likelihood using the EM algorithm and multiple imputation hold more promise for dealing with. With nonignorable missing data, the reasons for the missing observations depend on the values of those variables. In the asthma data, a censoring.When I refer to data sets in class or on homework, I will put pointers to the sets in this section.. EM algorithm - Examples from homework assignment 3: DA1.r: Data augmentation - Univariate incomplete data: DA2.r: Data augmentation - Regression with missing data: DA3.r: Data augmentation - Gaussian mixtures: DA4.r: Data augmentation.
EM Algorithm for Data with Missing Values The EM algorithm (Dempster, Laird, and Rubin 1977 ) is a technique that finds maximum likelihood estimates in parametric models for incomplete data. For a detailed description and applications of the EM algorithm, see the books by Little and Rubin ( 2002 ); Schafer ( 1997 ); McLachlan and Krishnan ( 1997 ).
