[SciPy-User] Least median of squares for regression in scipy?

[Jason Rennie]

"least median of squares" doesn't mean anything to me.  But, I know that
minimizing sum of absolute differences will provide a good estimate of the
median and is a good technique for dealing with outliers:

http://en.wikipedia.org/wiki/Least_absolute_deviation
http://en.wikipedia.org/wiki/L1_norm

Note that you'll need an LP solver.  Another option is a hybrid between the
squared and absolute value loss functions, such as the one that Peter Huber
devised:

http://en.wikipedia.org/wiki/Huber_Loss_Function

This loss provides the outlier-insensitivity of L1 while being easy to solve
using gradient descent & line search.

Cheers,

Jason

On Tue, Aug 24, 2010 at 9:17 AM, Jorge Scandaliaris
<jorgesmbox-ml at yahoo.es>wrote:

> Hi,
> I have to perform a linear regression on noisy data. On the last paper I
> read
> least median of squares was suggested for dealing with outliers. I have
> searched
> the scipy docs but it seems nothing is readily available. Searching the web
> for
> "(python OR scipy OR numpy) least median square" doesn't yield meaningfull
> results. The best I found were fortran and matlab code, which I would need
> to
> wrap (I have zero knowledge about fortran or wrapping it into python,
> except
> that there's a tool called f2py, but I would have to learn that as well) or
> rewrite (I used matlab in the past, so this should be feasible).
> I am asking here in the hope there's something I overlooked before I jump
> into
> one of the (probably more time demanding) possibilities I mentioned above.
>
> Thanks in advance,
>
> Jorge
>
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-- 
Jason Rennie
Research Scientist, ITA Software
617-714-2645
http://www.itasoftware.com/
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