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Re: st: After non-convergence with xtmixed

From   Clyde Schechter <[email protected]>
To   [email protected]
Subject   Re: st: After non-convergence with xtmixed
Date   Wed, 02 Jan 2008 16:29:19 -0500

Thanks to Maarten Buis for his suggestions.

I wasn't actually thinking about using the results of an uncompleted attempt at estimation as a final result to report in a paper. It was precisely for the purpose of diagnosing what is wrong with the model that I thought the results corresponding to the final state of the gradient-based estimation might be more helpful than the final EM-iteration results.

In particular, in addition to the kinds of modeling problems Maarten pointed out, xtmixed can fail to converge because of a boundary problem when one of the random effects being estimated is close to zero. In some of my models this might be the case. But the final EM results may be fairly far from the correct values and could fail to display this problem, could they not?

Of course, it is simple enough to re-estimate the model leaving out the suspected offending random effect. But the fact that the reduced model converges isn't really evidence that the omitted component is close to zero, is it? So I'm left not really knowing if the reduced model is adequate.

That's why I thought that seeing the estimates based on the incomplete gradient-estimation would be more helpful, because typically the log likelihood ratio is much bigger and I would imagine that the corresponding random effect estimate would be a better way to judge if I'm up against a boundary problem.

Clyde Schechter

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