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From |
"JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Mixed model degrees of freedom and Stata presentation |

Date |
Sat, 17 Nov 2012 16:57:32 -0500 |

On Sat, Nov 17, 2012 at 4:05 PM, Jordan Silberman <silberman.stata@gmail.com> wrote: > > 3. The solution to this problem offered in Stata is to assume infinite > degrees of freedom. It seems to me, from a statistically naive > perspective, that it is literally mathematically impossible to use a > less defensible solution. It's not possible to provide a df estimate > that is further from the true df value than infinite. But I suspect > that there's more to it than this. Can anyone explain why assuming > that df = infinite is more defensible than other df estimation > methods, even though other methods are mathematically guaranteed to > provide more accurate df estimates? I don't have much to say about the other questions so I won't blather on about them, but the notion of assuming infinite DF isn't totally crazy. Think of using the normal distribution for a humble one sample t test. The normal is a t with infinite DF. When n > 30 it's not too far off and when n > 100 you aren't really benefitting from using the t distribution anymore. Most of the benefit of using DF comes when n is pretty small. Estimated DF are good on average (i.e., probably in a mean squared error sense), but under other losses they may not be so great. I do agree that it would be quite nice if there were other estimates available, though, if for no other reason than the potential for a journal reviewer's panties to get in a bunch about exactly which method was used. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Mixed model degrees of freedom and Stata presentation***From:*Jordan Silberman <silberman.stata@gmail.com>

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