Hi, I was wondering whether anyone could help me, I am currently investigating the EM Algorithm as a way of dealing with missing data, but it appears as if in order to do broad based/exploratory inference on the data I need to run the Data Augmentation algorithm after EM and then perform Multiple Imputation by combining my results with Rubins (1987) rules.
I was wondering if there is any other way of preforming imputations directly from the results of EM, ie. Assuming a multivariate normal model and obtain the MLE's via EM, then impute in some way a certain number of different data sets (directly from the multivarate normal with MLE's) and then after having done standard complete tests on the complete datasets to combine them using the same rules as MI. So in other words, what imputation techniques would allow me to use Rubins rules to yield valid inferences. Any help, anyone could give would be appreciated. Fay Hughes (Miss) Statistics Masters Student University of Natal Durban South Africa
