From: Matt Shotwell <matt_at_biostatmatt.com>

Date: Tue, 01 Mar 2011 09:35:10 -0500

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 01 Mar 2011 - 14:37:31 GMT

Date: Tue, 01 Mar 2011 09:35:10 -0500

Jim,

Thanks for pointing me to this article. The authors argue that the bootstrap intervals for a robust estimator may not be as robust as the estimator. In this context, robustness is measured by the breakdown point, which is supposed to measure robustness to outliers. Even so, the authors found that the upper bound of a quantile bootstrap interval for the sample median was nearly as robust as the sample median. That brings some comfort in using quantile bootstrap intervals in quantile regression.

Does the sandwich estimator assume that errors are independent? And a related question: Does the rq function allow the user to specify clusters/grouping among the observations?

Best,

Matt

On Tue, 2011-03-01 at 05:35 -0600, James Shaw wrote:

> Matt:

*>
**> Thanks for your prompt reply.
**>
**> The disparity between the bootstrap and sandwich variance estimates
**> derived when modeling the highly skewed outcome suggest that either
**> (A) the empirical robust variance estimator is underestimating the
**> variance or (B) the bootstrap is breaking down. The bootstrap
**> variance estimate of a robust location estimate is not necessarily
**> robust, see Statistics & Probability Letters 50 (2000) 49-53. Since
**> submitting my earlier post, I have noticed that the the robust kernel
**> variance estimate is similar to the bootstrap estimate. Under what
**> conditions would one expect Koenker and Machado's sandwich variance
**> estimator, which uses a local estimate of the sparsity, to fail?
**>
**> --
**> Jim
**>
**>
**>
**> On Mon, Feb 28, 2011 at 8:59 PM, Matt Shotwell <matt_at_biostatmatt.com> wrote:
**> > Jim,
**> >
**> > If repeated measurements on patients are correlated, then resampling all
**> > measurements independently induces an incorrect sampling distribution
**> > (=> incorrect variance) on a statistic of these data. One solution, as
**> > you mention, is the block or cluster bootstrap, which preserves the
**> > correlation among repeated observations in resamples. I don't
**> > immediately see why the cluster bootstrap is unsuitable.
**> >
**> > Beyond this, I would be concerned about *any* variance estimates that
**> > are blind to correlated observations.
**> >
**> > The bootstrap variance estimate may be larger than the asymptotic
**> > variance estimate, but that alone isn't evidence to favor one over the
**> > other.
**> >
**> > Also, I can't justify (to myself) why skew would hamper the quality of
**> > bootstrap variance estimates. I wonder how it affects the sandwich
**> > variance estimate...
**> >
**> > Best,
**> > Matt
**> >
**> > On Mon, 2011-02-28 at 17:50 -0600, James Shaw wrote:
**> >> I am fitting quantile regression models using data collected from a
**> >> sample of 124 patients. When modeling cross-sectional associations, I
**> >> have noticed that nonparametric bootstrap estimates of the variances
**> >> of parameter estimates are much greater in magnitude than the
**> >> empirical Huber estimates derived using summary.rq's "nid" option.
**> >> The outcome variable is severely skewed, and I am afraid that this may
**> >> be affecting the consistency of the bootstrap variance estimates. I
**> >> have read that the m out of n bootstrap can be used to overcome this
**> >> problem. However, this procedure requires both the original sample
**> >> (n) and the subsample (m) sizes to be large. The version implemented
**> >> in rq.boot does not appear to provide any improvement over the naive
**> >> bootstrap. Ultimately, I am interested in using median regression to
**> >> model changes in the outcome variable over time. Summary.rq's robust
**> >> variance estimator is not applicable to repeated-measures data. I
**> >> question whether the block (cluster) bootstrap variance estimator,
**> >> which can accommodate intraclass correlation, would perform well. Can
**> >> anyone suggest alternatives for variance estimation in this situation?
**> >> Regards,
**> >>
**> >> Jim
**> >>
**> >>
**> >> 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 266
**> >> Chicago, IL 60612
**> >> Tel.: 312-355-5666
**> >> Fax: 312-996-0868
**> >> Mobile Tel.: 215-852-3045
**> >>
**> >> ______________________________________________
**> >> R-help_at_r-project.org mailing list
**> >> https://stat.ethz.ch/mailman/listinfo/r-help
**> >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
**> >> and provide commented, minimal, self-contained, reproducible code.
**> >
**> >
**> >
**>
**>
**>
**> --
**> 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 266
**> Chicago, IL 60612
**> Tel.: 312-355-5666
**> Fax: 312-996-0868
**> Mobile Tel.: 215-852-3045
**>
**> ______________________________________________
**> R-help_at_r-project.org mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
**> and provide commented, minimal, self-contained, reproducible code.
*

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