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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: mata optimize with d2debug |

Date |
Fri, 25 Sep 2009 11:18:13 -0500 |

Austin, The ridge is everywhere... the model has ancillary parameters not identified under the null. Davies-Hansen-Andrews-Ploberger story, if you know what I mean (see some links at http://www.citeulike.org/user/ctacmo/tag/identification if you're interested). So in population I have an exactly flat ridge. What I have in the sample is of course a mystery of small samples, but at any rate it is not nice. Re-parameterization won't help much, although I have several options to play with. Partha, yes, that's pretty much the same problem as identification in FMM. There, if you test H0: N(0,1) vs. H1: (1-\theta) N(0,1) + \theta N(\mu,1), then \theta\mu = 0 under the null, and you cannot identify both parameters. The numeric derivatives are based on subtracting one sampling zero from another (O(1/\sqrt{n}), I think), so now wonder they are quite unstable. So your insight, although not great, is exactly relevant :)). Probably I cannot just do any better in this model. -- 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/

**References**:**st: mata optimize with d2debug***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: mata optimize with d2debug***From:*Partha Deb <partha.deb@hunter.cuny.edu>

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