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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 15:59:45 -0500 |

On Thu, Sep 17, 2009 at 3:22 PM, Partha Deb <partha.deb@hunter.cuny.edu> wrote: > Also, while EM might be able > to deal with multimodalities better than ML (esp. Stata's -ml-), there is > nothing inherent about EM that makes it "better". If anything, EM is actually worse than the brute force -ml- when it comes to multimodality and false convergence declaration. (I am surprised that I have to disagree with Imbens :)) There are known examples when EM converges to saddlepoints of the likelihood (-difficult- option would probably be able to pull maximization out of it, however). McLachlan & Krishnan (http://www.amazon.com/Algorithm-Extensions-Wiley-Probability-Statistics/dp/0471201707) give an example of this behavior (at least I remember one in the first edition, it would be strange if they took it away in the second edition). Also, since neither the likelihood nor its gradient are computed, the algorithm has to rely only on -tolerance- (in terms of Stata's -maximize-), which is the worst convergence criterion to work with. -- 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/

**Follow-Ups**:**RE: st: finite mixture models with the EM algorithm***From:*"Verkuilen, Jay" <JVerkuilen@gc.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>

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

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

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