[SciPy-User] Least median of squares for regression in scipy?
Tue Aug 24 11:03:05 CDT 2010
if I am not wrong you are looking for quantile regression, which has least
absolute deviation as a special case equivalent to median regression. There
is some matlab code on Roger Koenker's site to do this, and it should be
very easy to translate to python.
On Tue, Aug 24, 2010 at 16:51, Skipper Seabold <firstname.lastname@example.org> wrote:
> On Tue, Aug 24, 2010 at 10:16 AM, <email@example.com> wrote:
> > On Tue, Aug 24, 2010 at 10:06 AM, Jorge Scandaliaris
> > <firstname.lastname@example.org> wrote:
> >> <josef.pktd <at> gmail.com> writes:
> >>> scikits.statsmodels has robust linear model estimators, rlm
> >>> Josef
> >> Thanks, I'll check it out
> > The wikipedia link for Huber loss function, and
> > http://statsmodels.sourceforge.net/rlm.html might get you started.
> This, with your other link should get you moving. We have as of now
> only M-estimators with several weighting norms
> (http://statsmodels.sourceforge.net/rlm_techn1.html) and scale
> estimators that are proposed in your reference. The framework is
> there for extension using iteratively reweighted least squares, if you
> need to go down this route. Let me know if any of the documentation
> or code is not clear.
> > It should be possible to try out different options to see which
> > version can handle your outliers.
> > (I'm a bit busy and don't know rlm so well, but if you have any
> > questions, I could look at them tonight, or maybe Skipper is
> > available. Skipper worked on RLM.)
> > Josef
> >> Jorge
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