TY  - GEN
AU  - Claesken, Gerda
TI  - Model selection and model averaging 
SN  - 0521852250
U1  - 519.5 
PY  - 2008///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Statistical and probabilistic mathematics
N1  - Table of contents:

1 - Model selection: data examples and introduction 
2 - Akaike's information criterion 
3 - The Bayesian information criterion 
4 - A comparison of some selection methods 
5 - Bigger is not always better 
6 - The focussed information criterion 
7 - Frequentist and Bayesian model averaging 
8 - Lack-of-fit and goodness-of-fit tests 
9 - Model selection and averaging schemes in action 
10 - Further topics 


N2  - 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 frequent 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  - http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511790485
ER  -