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.
ISBN-13: 9780521852258
Media Type: Hardcover
Publisher: Cambridge University Press
Publication Date: 07-28-2008
Pages: 332
Product Dimensions: 7.20(w) x 10.00(h) x 1.10(d)
Series: Cambridge Series in Statistical and Probabilistic Mathematics #27
Gerda Claeskens is Professor in the OR and Business Statistics and Leuven Statistics Research Center at the Catholic University of Leuven, Belgium.
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.
Table of Contents
Preface; A guide to notation; 1. Model selection: data examples and introduction; 2. Akaike's information criterion; 3. The Bayesian information criterion; 4. A comparison of some selection methods; 5. Bigger is not always better; 6. The focussed information criterion; 7. Frequentist and Bayesian model averaging; 8. Lack-of-fit and goodness-of-fit tests; 9. Model selection and averaging schemes in action; 10. Further topics; Overview of data examples; Bibliography; Author index; Subject index.
