[RsR] Can robust estimators outperform least squares in nonlinear regression for pure Gaussian noise?

[Dr. Matthias Kohl]

Dear Eduardo,

I think a key point is the median-based efficiency criterion which you 
use to compare the estimators. In particular, do you know of a result 
which states that the least squares estimator is optimal with respect to 
median(|LS estimator - true parameter|)?

Beside that, I'm not convinced that this median-based efficiency 
criterion is a good choice for the comparison of estimators with the aim 
to investigate their robustness properties. But I will have a look at 
You (1999) in the next days.

Just my two cents.
Matthias

On 13.07.2010 05:45, Eduardo Concei��o wrote:
> Hi,
>
> I have recently conducted a Monte Carlo simulation study for robust univariate *nonlinear* regression estimators using small sample data taken from case studies in the chemical engineering field. The paper is available from doi:10.1016/j.compchemeng.2010.04.009
>
> A very unusual finding was that for *pure* Gaussian error some of the robust estimators could *outperform* the least squares estimator. Even though I do not known of any theoretical result which prevents this behavior to happen, I have never seen it reported either.
>
> I would like to known whether you find this acceptable or not and what you think might be causing it.
>
> Thanks in advance for your help.
>
> Eduardo L.T. Concei��o
> Dept. of Chemical Engineering
> University of Coimbra
> Portugal
> e-mail: econceicao using kanguru.pt; etc using eq.uc.pt
>
>    
>
>
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>    

-- 
Dr. Matthias Kohl
www.stamats.de


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