Re: [R] how to test robustness of correlation

From: Berton Gunter <gunter.berton_at_gene.com>
Date: Fri 27 Jan 2006 - 03:33:05 EST


One more thing ...

> I played around cor.rob(). Yes, I can get a robust correlation
> coefficient matrix based on mcd or mve outlier detection methods.
>
> I have two further questions:
>

You might call it semantics, but I prefer "resistant estimation" to "outlier detection methods." I recognize that they are equivalent (any resistant estimator can be used to identify "outliers"; any outlier detection method leads to a resistant estimator on downweighting of outliers). However, I consider the distinction important. "Outlier detection" suggests:

  1. That "outlier" is a statistically well-defined concept; it isn't. The implied dichotomy is a fiction (a dangerous one, IMO -- but many would disagree).
  2. That some sort of hypothesis testing procedure is used to "reject" points. None is.

Rather, mve() and mcd() try to characterize the behavior of the "central" mass of the distribution, using that characterization to weight the informativeness of points outside that mass. A 1-D equivalent is MAD for spread. This is a far cry from the bad old days of (sequential) "outlier detection." These methods are crucially dependent on modern computer power of course.

Cheers,

Bert



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