Model selection and model averaging /
Claeskens, Gerda, 1973-
Model selection and model averaging / Model Selection & Model Averaging Gerda Claeskens, Nils Lid Hjort. - Cambridge : Cambridge University Press, 2008. - 1 online resource (xvii, 312 pages) : digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ; 27 . - Cambridge series on statistical and probabilistic mathematics ; 27. .
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Model selection : data examples and introduction -- Akaike's information criterion -- The Bayesian information criterion -- A comparison of some selection methods -- Bigger is not always better -- The focussed information criterion -- Frequentist and Bayesian model averaging -- Lack-of-fit and goodness-of-fit tests -- Model selection and averaging schemes in action.
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.
9780511790485 (ebook)
Mathematical models--Research.
Mathematical statistics--Research.
Bayesian statistical decision theory.
QA276.18 / .C53 2008
519.5
Model selection and model averaging / Model Selection & Model Averaging Gerda Claeskens, Nils Lid Hjort. - Cambridge : Cambridge University Press, 2008. - 1 online resource (xvii, 312 pages) : digital, PDF file(s). - Cambridge series on statistical and probabilistic mathematics ; 27 . - Cambridge series on statistical and probabilistic mathematics ; 27. .
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Model selection : data examples and introduction -- Akaike's information criterion -- The Bayesian information criterion -- A comparison of some selection methods -- Bigger is not always better -- The focussed information criterion -- Frequentist and Bayesian model averaging -- Lack-of-fit and goodness-of-fit tests -- Model selection and averaging schemes in action.
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.
9780511790485 (ebook)
Mathematical models--Research.
Mathematical statistics--Research.
Bayesian statistical decision theory.
QA276.18 / .C53 2008
519.5