RE: [Impute] convergence of EM under collinearity

[Howells, William]

This is just off the top of my head, but I recall that MCMC is a better method because it allows for uncertainty in the parameters themselves (mean and variance), not only at the observation level.  MCMC uses the initial EM estimates as starting values so it might be interesting to see what happens with the means over a large number of iterations, say 1000, 5000 or 10,000, if you have the computing power and time.  PROC MI will plot the means and variances over the iterations and you can check if they “stabilize” (timeplot option on the MCMC statement).  What to conclude if MCMC converges but EM does not, or how that relates to collinearity in the model, I’m not sure.    Bill Howells, MS Wash U Med School, St Louis   From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Paul von Hippel Sent: Tuesday, June 21, 2005 3:00 PM To: impute@lists.utsouthwestern.edu Subject: [Impute] convergence of EM under collinearity   I am using SAS PROC MI with the default settings. When I include nearly-collinear variables in my imputation model, I commonly get messages like the following: WARNING: The EM algorithm (MLE) fails to converge after 200 iterations. You can increase the number of iterations (MAXITER= option) or increase the value of the convergence criterion (CONVERGE=option). NOTE: The EM algorithm (posterior mode) converges in 141 iterations. The messages go away if I remove some of the nearly-collinear variables, but I would like to keep those variables since I need them for analysis. Looking at the the messages, I would say that SAS's implementation of the EM algorithm has two stages: the first stage estimates the MLE; the second stage estimates the posterior mode. It also appears that the second stage converged even though the first stage didn't. (Possibly the second stage benefited from a default prior.) I don't find any of this discussed in the documentation. Would you agree with my interpretation? Also, would you expect it's safe to use the imputed data despite the warning? Since the second stage of EM converged, I'm thinking that the imputed data may be okay. Many thanks for any advice. Paul von Hippel Paul von Hippel Department of Sociology / Initiative in Population Research Ohio State University The materials in this message are private and may contain Protected Healthcare Information. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return mail.

_______________________________________________
Impute mailing list
Impute@lists.utsouthwestern.edu
http://lists.utsouthwestern.edu/mailman/listinfo/impute