Message-ID: <201007130345.o6D3j1Hb005177@wallabie.isp.ip.pt>
Date: 2010-07-13T03:45:01Z
From: Eduardo Conceicao
Subject: [RsR] Can robust estimators outperform least squares in nonlinear regression for pure Gaussian noise?
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 at kanguru.pt; etc at eq.uc.pt