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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Cluster-correlated robust variance estimator for M regression |

Date |
Tue, 13 Oct 2009 12:49:32 -0500 |

If you are familiar with the theory of M-estimates, you could write the scores functions from -rreg-. Fiddle with the rreg.ado code a little bit, it's a pretty cute program. After everything is converged, but before everything is dropped, you would need to add a line like gen _score = `res'/`scale'*`weight' (of course you would need to figure out what the derivative of the objective function with respect to the parameter is exactly, and what all these internals of -rreg- mean). Then feed these scores into -_robust- (you might have to change some of the dated -estimates- command, which are now -ereturn- commands, to make -_robust- understand what's going on). I don't think there's a better way of doing that. In fact, I won't be sure that -jackknife- will even be consistent for this, as it may have difficulty with the not-so-smooth objective functions used in -rreg-. I am personally not very convinced by the idea of running -rreg- on longitudinal data. My thinking is: "-rreg- is a maximum likelihood estimator for the distribution of the errors that has a smooth normal-like density near zero, and exponentially decaying Laplace-like density in the tails. And as the maximum likelihood estimator, it assumes the data to be i.i.d. When you have a random effect working on top of it, you will likely lose efficiency quite a bit by misspecifying the (quasi-)likelihood to be that of i.i.d. data". But that's a complicated argument, you know your model is most likely wrong, anyway :)). On Tue, Oct 13, 2009 at 10:40 AM, James Shaw <shawjw@gmail.com> wrote: > Dear Statalist Members: > > I am interested in fitting a regression model using the M estimator > (-rreg- in Stata) to longitudinal data. I would like to apply the > cluster-robust variance estimator to account for arbitrary > intraclass/intracluster correlation. Is there any way to derive > cluster-robust > variance estimates for M estimates short of using the jackknife or > bootstrap? I'd like to avoid resampling procedures if possible. > -rreg- does not allow for the prediction of scores. Otherwise, I > would use _robust or -suest- or do the necessary programming myself. > > Thank you for your assistance. > > Regards, > > James W. Shaw, Ph.D., Pharm.D., M.P.H. > Assistant Professor > Department of Pharmacy Administration > College of Pharmacy > University of Illinois at Chicago > 833 South Wood Street, M/C 871, Room 252 > Chicago, IL 60612 > * > * 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/

**Follow-Ups**:**Re: st: Cluster-correlated robust variance estimator for M regression***From:*James Shaw <shawjw@gmail.com>

**References**:**st: Cluster-correlated robust variance estimator for M regression***From:*James Shaw <shawjw@gmail.com>

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