I also think it is better to start with a mad. If the initial scale
estimate is to small, the residuals can indeed become large and every
observation be assigned a null weight.
Le 5 févr. 2014 19:06, "Josef Perktold" <notifications@github.com> a écrit :

> In statsmodels/robust/robust_linear_model.py:
>
> > @@ -261,6 +261,7 @@ def fit(self, maxiter=50, tol=1e-8, scale_est='mad', init=None, cov='H1',
> >
> >          wls_results = lm.WLS(self.endog, self.exog).fit()
> >          if not init:
> > +            self.scale = wls_results.scale
>
> That's the only point where I have conceptual problems.
> wls_results.scale is not a robust estimator, It could be far off if there
> are large outliers.
> I think we should still start with mad, center=0
>
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>