Model Selection and Model Averaging
1st Edition
Published on 30. September 2008
978-0-511-42123-5 (ISBN)
Description
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.
Reviews / Votes
'This is a good textbook for a master-level statistical course about model selection.' Mathematical Reviews '... given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make [a] difference.' MAA Reviews '... the authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection.' Statistics in SocietyMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
Worked examples or Exercises; 35 Tables, unspecified; 1 Halftones, unspecified; 45 Line drawings, unspecified
File size
2,76 MB
ISBN-13
978-0-511-42123-5 (9780511421235)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Gerda Claeskens | Nils Lid Hjort
Model Selection and Model Averaging
Book
07/2008
Cambridge University Press
€105.90
Shipment within 15-20 days
Persons
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.
Content
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.
