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Re: st: finite mixture models with the EM algorithm


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: finite mixture models with the EM algorithm
Date   Thu, 17 Sep 2009 13:49:08 -0500

The EM algorithm might converge faster, that's all. You will get the
same estimates, although without the standard errors; they are the
pain in the lower back with the EM. If -fmm- works for you, there is
little reason to try and code the EM algorithm from scratch, unless
you'd be lucky to find an implementation floating around. Unlike the
general -ml-, the EM algorithm is problem-specific and depends on what
your missing data are. In -xtmixed-, the missing data are continuous
random effects, while in mixture models, the missing data are discrete
class indicators. The E-steps are entirely different in those two
problems.

2009/9/17 László Sándor <sandorl@gmail.com>:
> Dear fellow statalisters,
>
> I am estimating a model where I assume that the dependent variable
> came from either of two classical normal linear models, with some
> probabilities for the one and the other. This is a finite mixture
> model, and I had some success with Partha Deb's -fmm- command.
>
> However, I heard it might be very much worthwhile to try the
> estimation using the EM algorithm, not standard (though fine-tuned á
> la Stata) ML.  Before any more Google searches getting lost among
> latent class models and various related topics, I would ask you
> whether you happen to know a tool (or way) that can do this estimation
> using the EM algorithm.
>
> (I know that there are different software packages claiming to do
> this, but I would rather stick to Stata. Especially because Stata does
> EM for -xtmixed-, for instance, so maybe it is also easily applicable
> to my problem somehow.)
>
> Thank you very much, you're help is very much appreciated, as always.
>
> Laszlo
>
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>



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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