TY  - BOOK
AU  - Claeskens,Gerda
AU  - Hjort,Nils Lid
TI  - Model selection and model averaging
T2  - Cambridge series on statistical and probabilistic mathematics
SN  - 9780511790485 (ebook)
AV  - QA276.18 .C53 2008
U1  - 519.5 22
PY  - 2008///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Mathematical models
KW  - Research
KW  - Mathematical statistics
KW  - Bayesian statistical decision theory
N1  - 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
N2  - 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
UR  - https://doi.org/10.1017/CBO9780511790485
ER  -