[RsR] Non-linear robust method

[Martin Maechler]

>>>>> "BLG" == Bruno L Giordano <bruno.giordano using music.mcgill.ca>
>>>>>     on Fri, 24 Aug 2007 12:33:45 -0400 writes:

    BLG> Hello,
    BLG> as a side note, the Matlab function nlinfit (Statistics toolbox) for 
    BLG> nonlinear fitting has a robust option.

    BLG> It shouldn't be incredibly hard to translate it in R code.

    BLG> However, I have to say that the routine itself does not perform 
    BLG> incredibly well in case of outliers.

But the  robustbase package *has* had robust nonlinear
regression, almost since its beginning,
 nlrob() !

Probably because Alex used a somewhat misleading subject line in
his posting, I think you (Arnold and Bruno) have both been answering
the wrong question.

If I understand correctly, Alex was rather looking for help on
doing robust modelling for a specified non-*normal* distribution 
``for the good data'', whereas most available robust functions
assume that the "good data" is normally distributed and then
there's a fraction of "arbitrarily distributed" data points
(sometimes called "outliers" ...).

IIUC, Alex wants the "good data" to be Pareto ...
and he mentioned Marazzi's code and papers which did this for
the Gamma (and 'Weibull', BTW).

Martin Maechler,
ETH ZUrich