[amelia] question about bootstrappping EM

Hello - My understanding of these methods is very basic, but I was hoping someone could offer some clarification on the specifics of the imputation procedure used in AMELIA II. I understand that in single regression imputation variances are often underestimated because the variable with missing data is a perfect linear function of the other variables used to impute it (correct?). However, I gather that AMELIA corrects for this in three ways: 1) First, by using an EM algorithm which adds a residual term to the variance 2) by adding a random component to reflect uncertainty in the missing data 3) using multiple imputed data sets to reflect this uncertainty as well. Is this correct or is this too basic an understanding? I would appreciate any help that could be offered. Thank you. - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia