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Model selection and model averaging

By: Claesken, Gerda.
Series: Cambridge Series in Statistical and Probabilistic Mathematics; 27. Publisher: Cambridge Cambridge University Press 2008Description: xviii, 312 p.ISBN: 0521852250; 9780511790485.Subject(s): Statistical and probabilistic mathematicsDDC classification: 519.5 Online resources: E-Book Summary: 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.
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Item type Current location Collection Call number Status Date due Barcode
Electronic Resources Vikram Sarabhai Library
Electronic Resources
Non-fiction 519.5 C5M6 (Browse shelf) Available ER000497

Table of contents:

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

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.

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