[RsR] Robust location estimator - an interesting application in finance

[Martin Maechler]

>>>>> "ARu" == Andreas Ruckstuhl <rkst using zhaw.ch>
>>>>>     on Mon, 28 Sep 2009 09:23:56 +0200 writes:

    ARu> Dear Ajay
    ARu> I agree on what Matias said.

    ARu> In addition, I wondered whether you should not better use a "direct" 
    ARu> robust location estimator instead of the robust location estimator based 
    ARu> on the regression case. For example, there is R function huberM() which 
    ARu> returns a robust M-estimation of location based on a safe way of 
    ARu> calculating the robust scale estimator. An additional advantage of using 
    ARu> huberM() is, that the resulting M-estimator has breakdown-point 0.5 
    ARu> which is (much) higher than that of a trimmed mean.

    ARu> To sell the robust M-estimator, you can talk of "an estimator which 
    ARu> down-weights outliers automatically according to their outlyingness".
    ARu> (The M-estimator can also be modified that it removes very distant 
    ARu> outliers completely by using a redescending psi-function.)

Yes, indeed, thank you, Andreas,
I was also going to propose using  huberM(),
which I had written a few years ago, exactly for dealing with
the "degenerate" case of having more than half of the
observations being equal.

Nonetheless, Ajay, I'm grateful for your bug report!

Regards,
Martin Maechler, ETH Zurich