Model selection and model averaging /

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Bibliographic Details
Author / Creator:Claeskens, Gerda, 1973- author.
Imprint:Cambridge ; New York : Cambridge University Press, 2008.
©2008
Description:1 online resource (xvii, 312 pages) : illustrations
Language:English
Series:Cambridge series in statistical and probabilistic mathematics
Cambridge series on statistical and probabilistic mathematics.
Subject:Mathematical models -- Research.
Mathematical statistics -- Research.
Bayesian statistical decision theory.
Modèles mathématiques -- Recherche.
Statistique mathématique -- Recherche.
Statistique bayésienne.
MATHEMATICS -- Probability & Statistics -- General.
Bayesian statistical decision theory.
Mathematical models -- Research.
Mathematical statistics -- Research.
Statistisches Modell
Statistisches Modell.
Electronic books.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11814088
Hidden Bibliographic Details
Other authors / contributors:Hjort, Nils Lid, author.
ISBN:9780511424106
0511424108
0511423624
9780511423628
9780511422430
0511422431
9780511790485
0511790481
9780511421235
0511421230
0511423098
9780511423093
9780521852258
0521852250
Notes:Includes bibliographical references (pages 293-305) and indexes.
Print version record.
Summary: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 suitable model is central to all statistical work with data. Selecting the variables for use in a regression model is one important example. The past two decades have seen rapid advances both in our ability to fit models and in the theoretical understanding of model selection needed to harness this ability, yet this book is the first to provide a synthesis of research from this active field, and it contains much material previously difficult or impossible to find. In addition, it gives practical advice to the researcher confronted with conflicting results." "Model choice criteria are explained, discussed and compared, including Akaike's information criterion AIC, the Bayesian information criterion BIC and the focused information criterion FIC. Importantly, the uncertainties involved with model selection are addressed, with discussions of frequentist and Bayesian methods. Finally, model averaging schemes, which combine the strengths of several candidate models, are presented."--Jacket.
Other form:Print version: Claeskens, Gerda, 1973- Model selection and model averaging. Cambridge ; New York : Cambridge University Press, 2008 9780521852258 0521852250
Standard no.:9786611791186