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Old Yesterday, 02:45 AM
relaxiii relaxiii is offline
Join Date: Aug 2016
Posts: 18

I have a question about the sample problems;

#7. You are given the following statements about iterative numerical optimization algorithms to estimate the covariance parameters of a Linear Mixed Model.

I. The expectation-maximization algorithm tends to overestimate the covariance of the parameters.

CAS says this is true, but the text says:
"In addition, the precision of estimators derived from the EM algorithm is overly optimistic, because the estimators are based on the likelihood from the last maximization step, which uses complete data instead of observed data."

Doesn't that mean we're underestimating the covariance of the parameters, since EM calculates them as being more precise than they actually are? I'm confused
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Old Yesterday, 03:44 AM
relaxiii relaxiii is offline
Join Date: Aug 2016
Posts: 18

Or is it because we're filling in missing data using the expected values as part of EM that we overestimate covariances bc what we use to fill in the holes comes from our own model & parameters are more likely to be correlated? lol ah
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