[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 Using this code:

    from sklearn.linear_model.theil_sen import _spatial_median
    n_samples = 100000  
    n_features = 1
    random_state = np.random.RandomState(0)
    X = random_state.normal(size=(n_samples, n_features))
    %timeit _spatial_median(X)

I got `100 loops, best of 3: 17.7 ms per loop` if I did not handle the 1d-case explicitely. Handling it explicetly like you suggested I got only `1000 loops, best of 3: 1.95 ms per loop`. So I guess we keep it. If we think about using the spatial median in other cases as for instance in the `median_absolute_error` for multi-output I think the case should be handled explicitely to ensure that the result is equal to the 1d median and not only approximately equal.