[R] how to test robustness of correlation

Berton Gunter gunter.berton at gene.com
Thu Jan 26 17:33:05 CET 2006

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

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.



More information about the R-help mailing list