[RsR] Non-linear robust method

[Ruckstuhl Andreas (rkst)]

Some additional remarks to Martin's and Eva's answer:
See Alfio Marazzi's page on Software: 
	 http://www.iumsp.ch/Unites/us/Alfio/msp_programmes.htm

See also the Maria-pia Victora-Feser's page
	http://www.hec.unige.ch/www/index.php?&pid=189

and there the papers 
	Dupuis, D. and M.-P. Victoria-Feser (2007). "A Robust Prediction Error Criterion for Pareto Modeling of Upper Tails". The Canadian Journal of Statistics. To appear. (proof's version)

	Maria-Pia Victoria-Feser, Elvezio Ronchetti (1997). "Robust Estimation for Grouped Data"
	  Journal of the American Statistical Association, Vol. 92, No. 437 (Mar., 1997), pp. 333-340


All the best
Andreas Ruckstuhl




>> -----Ursprüngliche Nachricht-----
>> Von: r-sig-robust-bounces using r-project.org 
>> [mailto:r-sig-robust-bounces using r-project.org] Im Auftrag von 
>> Martin Maechler
>> Gesendet: Freitag, 24. August 2007 19:18
>> An: Bruno L. Giordano
>> Cc: Stromberg, Arnold; r-sig-robust using r-project.org; 
>> A.Teytelboym using lse.ac.uk
>> Betreff: Re: [RsR] Non-linear robust method
>> 
>> 
>> >>>>> "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
>> 
>> _______________________________________________
>> R-SIG-Robust using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
>>