algorithmic method quantile regression

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algorithmic method quantile regression

T.Riedle
Greetings R Community,
I am trying to run a quantile regression using the quantreg package. My regression includes 7 independent variables with approx. 800 daily observations each. Thus, I think that the Barrodale and Roberts algorithm should do the trick. However, the Frisch-Newton after preprocessing returns different results and more significant coefficients than the br method. Which algorithmic method should I use now? Do the results mean that the Frisch-Newton after preprocessing dominates the br method?

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Re: algorithmic method quantile regression

Roger Koenker-3
Did you read item 1 in the quantreg FAQ()?  


url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    [hidden email]            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801

> On Oct 14, 2015, at 2:56 PM, T.Riedle <[hidden email]> wrote:
>
> Greetings R Community,
> I am trying to run a quantile regression using the quantreg package. My regression includes 7 independent variables with approx. 800 daily observations each. Thus, I think that the Barrodale and Roberts algorithm should do the trick. However, the Frisch-Newton after preprocessing returns different results and more significant coefficients than the br method. Which algorithmic method should I use now? Do the results mean that the Frisch-Newton after preprocessing dominates the br method?
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> 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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Re: algorithmic method quantile regression

T.Riedle
The fn and br methods return the same results but the results provided by pfn differ. I do not find an explanation for this observation in the papers on quantile regression. Therefore my question.

-----Original Message-----
From: Roger Koenker [mailto:[hidden email]]
Sent: 14 October 2015 22:33
To: T.Riedle
Cc: [hidden email]
Subject: Re: [R] algorithmic method quantile regression

Did you read item 1 in the quantreg FAQ()?  


url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    [hidden email]            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801

> On Oct 14, 2015, at 2:56 PM, T.Riedle <[hidden email]> wrote:
>
> Greetings R Community,
> I am trying to run a quantile regression using the quantreg package. My regression includes 7 independent variables with approx. 800 daily observations each. Thus, I think that the Barrodale and Roberts algorithm should do the trick. However, the Frisch-Newton after preprocessing returns different results and more significant coefficients than the br method. Which algorithmic method should I use now? Do the results mean that the Frisch-Newton after preprocessing dominates the br method?
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> 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.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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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Re: algorithmic method quantile regression

Cade, Brian
>From ?rq.fit.pfn I see:

Details:

     Preprocessing algorithm to reduce the effective sample size for QR
     problems with (plausibly) iid samples.  The preprocessing relies
     on subsampling of the original data, so situations in which the
     observations are not plausibly iid, are likely to cause problems.
     The tolerance eps may be relaxed somewhat.

And from 1 in the Quantreg FAQ that Roger indicated there is:

>From ?rq.fit.fn:

   eps: tolerance parameter for convergence.  In cases of multiple
          optimal solutions there may be some discrepancy between
          solutions produced by method '"fn"' and method '"br"'.  This
          is due to the fact that '"fn"' tends to converge to a point
          near the centroid of the solution set, while '"br"' stops at
          a vertex of the set.



So it sounds like the pfn version of quantile regression estimates might
differ because it is intended for independent identically distributed data
(think homogeneous), it involves subsampling of the data set, and
convergence criteria are slightly different for fn and pfn than for the br
(standard Barrodale and Roberts simplex linear program) algorithm.  All
conditions that could lead to different estimates.  My recommendation for
the sample sizes you are considering is to stick with the Barrodale and
Roberts algorithm as it is the best understood, most reliable procedure.

Brian


Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  [hidden email] <[hidden email]>
tel:  970 226-9326


On Wed, Oct 14, 2015 at 3:03 PM, T.Riedle <[hidden email]> wrote:

> The fn and br methods return the same results but the results provided by
> pfn differ. I do not find an explanation for this observation in the papers
> on quantile regression. Therefore my question.
>
> -----Original Message-----
> From: Roger Koenker [mailto:[hidden email]]
> Sent: 14 October 2015 22:33
> To: T.Riedle
> Cc: [hidden email]
> Subject: Re: [R] algorithmic method quantile regression
>
> Did you read item 1 in the quantreg FAQ()?
>
>
> url:    www.econ.uiuc.edu/~roger            Roger Koenker
> email    [hidden email]            Department of Economics
> vox:     217-333-4558                University of Illinois
> fax:       217-244-6678                Urbana, IL 61801
>
> > On Oct 14, 2015, at 2:56 PM, T.Riedle <[hidden email]> wrote:
> >
> > Greetings R Community,
> > I am trying to run a quantile regression using the quantreg package. My
> regression includes 7 independent variables with approx. 800 daily
> observations each. Thus, I think that the Barrodale and Roberts algorithm
> should do the trick. However, the Frisch-Newton after preprocessing returns
> different results and more significant coefficients than the br method.
> Which algorithmic method should I use now? Do the results mean that the
> Frisch-Newton after preprocessing dominates the br method?
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> > 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.
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> 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.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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.