[MRG+1] TheilSen robust linear regression

A multiple linear Theil-Sen regression for the Scikit-Learn toolbox. The implementation is based on the algorithm from the paper "Theil-Sen Estimators in a Multiple Linear Regression Model" of Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang. It is parallelized with the help of joblib.
On a personal note, I think that the popular Theil-Sen regression would be a nice addition to Scikit-Learn.
I am looking forward to your feedback. 

Florian
@ogrisel From Wikipedia: "In statistics, a parametric model or parametric family or finite-dimensional model is a family of distributions that can be described using a finite number of parameters."
Least squares for instance makes the assumption that the error is normally distributed. The parameters of this distribution is then found by maximum likelihood. At least this is one way to derive the least squares method. On the other hand, Theil-Sen makes no assumption about the underlying distribution, the method does not try to find the parameters of some distribution and is therefore considered non-parametric although some parameters are of course determined but not of a distribution.
I think that is just about how one defines parametric and non-parametric. Can I keep it that way since this is consistent with the definition in statistics?