Model Selection and Model Averaging

Cambridge Series in Statistical and Probabilistic Mathematics #27

by Gerda Claeskens, Nils Lid Hjort

Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled with discussions of frequent and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R-code.

332 pages, Hardcover

First published July 28, 2008

Reviews

[Nils Lid Hjort · Rating 5 out of 5]

I'm the author, with my colleague Gerda Claeskens from KU Leuven, Belgium, and hence rather biased. But I'm happy with this Cambridge University Press book, from 2008, and do think it has earned its firm place in the literature on statistical model selection and model averaging. We do the AIC, BIC, DIC, for the Akaike, the Bayesian, and the Deviance Information Criteria, and push Gerda's and my own FIC, the focused Information Criterion.

For exercises & stories, featuring model selection, see my FocuStat webpage and the University of Oslo, and also the course website for the course STK 4160, with supplementary exercises and other material.

https://perswww.kuleuven.be/~u0043181/

and

https://www.uio.no/studier/emner/matn...