Model Selection and Model Averaging

Erscheinungsjahr 2014
ISBN: 978-0-521-85225-8
Verlag: Cambridge University Press

Buch, Englisch, Band 27, 320 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 821 g

Reihe: Cambridge Series in Statistical and Probabilistic Mathematics

Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? 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 frequentist 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.

Autoren/Hrsg.

Claeskens, Gerda

Hjort, Nils Lid

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

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.

Über Autor(innen)

Claeskens, Gerda
Gerda Claeskens is Professor in the OR and Business Statistics and Leuven Statistics Research Center at the Catholic University of Leuven, Belgium.

Hjort, Nils Lid
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.