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

[Jorge Scandaliaris]

Jason Rennie <jrennie <at> gmail.com> writes:

> 
> 
> "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.
> 

Thanks, I am not very proficient with the subject, I am just trying to use it as
a tool in a very particular task. I'll check the links you provide. BTW, this
reference is what I was taking about  with "least median of squares":
http://www.stats.uwo.ca/faculty/aim/2004/04-259/ExtraReading/LeastMedianOfSquaresRegression.pdf

Regards,

Jorge