Actually, I realise from what I just wrote that the danger is to
underestimate the initial scale. If we overestimate it instead, it may take
one iteration more to converge (since the weights will all be 1 at the
first step) but IRLS would still succeed. On the contrary, if the scale is
underestimated, we are in the degenerated case I mentioned before.
I am afraid that if a sufficient number of observations fall on the
regression hyperplane, then mad could be equal to zero (could it?) and then
we are in trouble.

I am sorry, I am a bit tired :)
Le 5 févr. 2014 23:31, "Virgile Fritsch" <virgile.fritsch@gmail.com> a
écrit :

> 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
>>
>> —
>> Reply to this email directly or view it on GitHub<https://github.com/statsmodels/statsmodels/pull/1355/files#r9470850>
>> .
>>
>