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From |
James Shaw <shawjw@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 13 Oct 2009 13:05:35 -0500 |

Thanks for the prompt response. I suspected that the estimated weights might factor into generating the scores, though I was not sure how to implement them. So, should I interpret the "scale" in your code to be the derivative of the objective function with respect to the parameters of interest? I generally agree with your concerns about the jackknife, though it seems to yield estimates that are similar to those provided by the bootstrap in this case. I am not a huge fan of M estimation. The only benefit it provides over quantile regression is efficiency, and the resulting estimates lack the interpretability of quantile regression estimates. I am in need of a robust estimator for longitudinal data that will not entail resampling for variance estimation since I am resampling at another stage in my code. Unfortunately, my options are limited. -- Jim On Tue, Oct 13, 2009 at 12:49 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > 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/ > -- 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/

**Follow-Ups**:**Re: st: Cluster-correlated robust variance estimator for M regression***From:*Stas Kolenikov <skolenik@gmail.com>

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

**Re: st: Cluster-correlated robust variance estimator for M regression***From:*Stas Kolenikov <skolenik@gmail.com>

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