[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
@arjoly I took this algorithm from the cited paper and wanted to make it easy for the reader to compare the implementation with the algorithm in the paper where it is called eta. Should I call it `indicator` instead? Coming from a mathematical background, I think that quite often there is no good name for a variable, it draws its meaning from its definition. Also, from my point of view, small variable names make an algorithm easier to read. For instance, we call our design matrix just `X` and the target just `y`, right? We could also call it `design_matrix` and `target` but an algebraic expression like `np.sqrt(np.sum((design_matrix.T - target)**2))` is harder to read than `np.sqrt(np.sum((X.T - y)**2))`. I also often use abbreviations like `nom` for nominator and `denom` for denominator. Of course, I can easily change it but I really think it makes the algorithm harder to read. @GaelVaroquaux What do you think about this?