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TY  - BOOK
ID  - 25177765
TI  - Model selection and model averaging
AU  - Claeskens, Gerda
AU  - Hjort, Nils Lid
PY  - 2008
VL  - 27
SN  - 9780521852258 0521852250 9780511790485 9780511424106 0511424108 0511423624 9780511423628 9780511422430 0511422431 0511790481 9780511421235 0511421230 0511423098 9780511423093 1107176204 1281791180 9786611791186 051142177X
PB  - Cambridge: Cambridge university press,
DB  - UniCat
KW  - Mathematical statistics
KW  - -Bayesian statistical decision theory.
KW  - Bayesian statistical decision theory
KW  - Mathematical models
KW  - 519.5
KW  - Academic collection
KW  - 519.2
KW  - 519.2 Probability. Mathematical statistics
KW  - Probability. Mathematical statistics
KW  - Mathematics
KW  - Statistical inference
KW  - Statistics, Mathematical
KW  - Statistics
KW  - Probabilities
KW  - Sampling (Statistics)
KW  - Models, Mathematical
KW  - Simulation methods
KW  - Bayes' solution
KW  - Bayesian analysis
KW  - Statistical decision
KW  - Research
KW  - Statistical methods
KW  - Bayesian statistical decision theory.
KW  - Research.
KW  - Statistique bayésienne
KW  - Mathematical Sciences
KW  - Probability
KW  - Mathematical models - Research
KW  - Mathematical statistics - Research
UR  - https://www.unicat.be/uniCat?func=search&query=sysid:25177765
AB  - 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.
ER  -