Re: [R] High breakdown/efficiency statistics -- was RE: Rosner's test

From: Martin Maechler <maechler_at_stat.math.ethz.ch>
Date: Fri 23 Jun 2006 - 18:42:58 EST

I'm CC'ing this to the R-SIG-robust mailing list  [R Special Interest Group on robust statistics] so it's properly archived there as well. Follow up ideally should only go there.

{BTW: Did you know that to *search* mailing list archives of

      such R-SIG-foo mailing lists, you can use google very
      efficiently by prepending the mailing list name and 'site:stat.ethz.ch'?
      e.g., use google search on
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>>>>> "BertG" == Berton Gunter <gunter.berton@gene.com>
>>>>> on Thu, 22 Jun 2006 09:44:33 -0700 writes: BertG> Many thanks for this Martin. There now are several BertG> packages with what appear to be overlapping functions
    BertG> (or at least algorithms). Besides those you     BertG> mentioned, "robust" and "roblm" are at least two others.

actually quite particular ones:

   At the moment it only has "robust covariance + location", but    when it will contain everything from its S-plus counterpart,    it will be a very nice benchmark; in many parts "first rate".

    BertG> Any recommendations about how or whether to
    BertG> choose among these for us enthusiastic but non-expert
    BertG> users?

As I said (in reply to Andy's suggestion) there will be a CRAN task view "real soon now"
in order to give some guidance on the diverse packages with robustness functionality.

    BertG> Cheers, Bert  

    >> -----Original Message----- From: Martin Maechler
    >> [mailto:maechler@stat.math.ethz.ch] Sent: Thursday, June
    >> 22, 2006 2:04 AM To: Berton Gunter Cc: 'Robert Powell';
    >> r-help@stat.math.ethz.ch Subject: Re: [R] Rosner's test
    >> 
    >> >>>>> "BertG" == Berton Gunter <gunter.berton@gene.com>
    >> >>>>> on Tue, 13 Jun 2006 14:34:48 -0700 writes:
    >> 

    BertG> RSiteSearch('Rosner') ?RSiteSearch or search directly     BertG> from CRAN.
    >>
    BertG> Incidentally, I'll repeat what I've said
    BertG> before. Don't do outlier tests.  They're
    BertG> dangerous. Use robust methods instead.
    >>  Yes, yes, yes!!!
    >> 
    >> Note that rlm() or cov.rob() from recommended package
    >> MASS will most probably be sufficient for your needs.
    >> 
    >> For slightly newer methodology, look at package
    >> 'robustbase', or also 'rrcov'.
    >> 

    >> Martin Maechler, ETH Zurich
    >>
    BertG> -- Bert Gunter Genentech Non-Clinical Statistics     BertG> South San Francisco, CA
    >>
    BertG> "The business of the statistician is to catalyze the     BertG> scientific learning process." - George E. P. Box     >>

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