[R] rlm/M/MM-estimator questions

Matthias Kohl Matthias.Kohl at uni-bayreuth.de
Thu Jul 7 09:49:03 CEST 2005

Christian Hennig wrote:

>Hi list,
>1) How can the MM-estimator method="MM" in function rlm be tuned to 85%
>efficiency? It seems that there is a default tuning to 95%. I presume, but
>am not sure, that the MM-estimator uses phi=phi.bisquare as default and
>the tuning constant could be set by adding a parameter c=...
>Is this true? Which value to use for 85%?
>(In principle I should be able to figure that out theoretically, but it
>would be much easier if somebody already knew the constant or a
>straightforward way to compute it.)
Hi Christian,

I have not calculated the efficiency myself ...
But the thesis of  Matias Salibian-Barrera (SB 2000) might help you to 
find the answer
(cf. Chapter 4).
See: http://mathstat.math.carleton.ca:16080/~matias/thesis.pdf

As far as I understand the choice k0=1.548 is to obtain a breakdown 
point 0.5 whereas k0=1.988 leads to a breakdown point of 0.4 - at least 
in the location case; confer p. 60 of SB 2000.

In the article "Optimal robust $M$-estimates of location" by Fraiman, 
Yohai and Zamar (Ann. Stat. 29(1): 194 - 223) which is, of course, 
concerned with the location case, the authors recommend to use k0=1.988 
instead of k0=1.548 (cf. p. 206).

Hope that helps!

>2) The M-estimator with bisquare uses "rescaled MAD of the residuals" as
>scale estimator according to the rlm help page. Does this mean that a
>scale estimator is used which is computed from least squares residuals? Are
>M-estimator and residual scale estimator iterated until convergence of
>them both? (Does this converge?) Or what else? What does "rescaled" mean?
>Thank you,
>*** NEW ADDRESS! ***
>Christian Hennig
>University College London, Department of Statistical Science
>Gower St., London WC1E 6BT, phone +44 207 679 1698
>chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
>R-help at stat.math.ethz.ch mailing list
>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

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