Model Selection and Model Averaging
Gerda Claeskens · Nils Lid Hjort
07/2008 · Cambridge University Press
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312
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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.
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 see...
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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.
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.
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