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From |
László Sándor <sandorl@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: finite mixture models with the EM algorithm |

Date |
Thu, 17 Sep 2009 14:59:16 -0400 |

Thanks very much, Stas! I just heard today in lecture from Guido Imbens that EM might solve the problem when the likelihood is multimodal, as in the case of mixtures. Can we reasonably expect Stata's ML procedure to solve this problem anyway? With -fmm-, convergence seemed to be really tricky for me. I had to dig deep and enable -ml search- and set a large number of repetitions for it. Plus I have the difficult option enabled. Even then, sometimes I get no convergence just telling me that the likelihood is concave, or some of the models converged to a case with basically only one component present out of the possible two -- which is not impossible, but unlikely in my case. Thanks again, Laszlo On Thu, Sep 17, 2009 at 2:49 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > 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 >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > > > -- > Stas Kolenikov, also found at http://stas.kolenikov.name > Small print: I use this email account for mailing lists only. > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: finite mixture models with the EM algorithm***From:*Partha Deb <partha.deb@hunter.cuny.edu>

**References**:**st: finite mixture models with the EM algorithm***From:*László Sándor <sandorl@gmail.com>

**Re: st: finite mixture models with the EM algorithm***From:*Stas Kolenikov <skolenik@gmail.com>

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