what's so great about models?

1 August 2008

I really enjoyed reading Breiman 2001 (Statistical Science 3:199-215), in which he says (approximately) that statisticians are too concerned with "true models" and that they should focus on descriptive, flexible, nonparametric models (random forests, generalized additive models, etc etc) instead. Really can't do these topics justice here, but it just reminds me that there's a whole other school (or something) of statistical thought. Philosophically, it ties in with Peters' Critique for Ecology and (perhaps) Jim Clark's recent claims that we should give up on "low-dimensional" models and embrace nature in its noise and complexity [ok, perhaps a bit unfair but I'm in a hurry]. Reasons I haven't focused on such topics in the book: (1) I don't know them that well, so I can't really teach them to others; (2) they are much more complicated computationally, thus leading to a "user" perspective rather than a "builder" perspective (not necessarily a bad thing but not what I was aiming for); (3) they are typically very data-hungry, unsuited to many (but certainly not all) ecological data sets; (4) I like tying models to ecological theory. John Drake at UGA is from the Breimanish (Breimanesque) school, don't know if he has any interesting perspectives written on the topic …

Further thoughts (a few hours later): one way to compare the two statistical approaches is that the "classical" approach is trying to explain or test, while the more "modern" (all in quotation marks) approach wants to describe and predict: as I comment in the book, while these questions often have similar answers, they imply different statistical approaches — and the answers are not always the same. Another related topic is semi-mechanistic models, which Steve Ellner and Simon Wood have variously championed. More later (?)

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