A while back I posted on a cool new nonparametric method, which goes by the acronym “MINE”, for detecting associations between variables in multivariate datasets. The method can detect even nonlinear (and non-monotonic!) relationships between pairs of variables, and it provides a measure of the strength of the relationship analogous to the familiar R^2.
Turns out that this approach has some drawbacks, though, perhaps quite serious. Andrew Gelman’s blog has a good summary of recent commentary. Not surprisingly for such a flexible nonparametric method, it seems to lack power. But there may be other issues as well, to do with things like the scope and rigor of the proofs of the method’s statistical properties. I’m not qualified to pass judgment on how serious these issues are. But if you’re thinking of using this method, you should definitely click through and check out the commentary.
UPDATE: The MINE authors themselves show up in the comments, briefly addressing the issues I’ve raised and noting that they’ve posted a detailed reply to the comments they’ve received over on Andrew Gelman’s blog. Great to see authors and their readers engaging in such a productive and substantial discussion. So if you’re interested in the MINE method, and alternative approaches, you really ought to click through to Andrew Gelman’s blog.