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Model Selection and Model Averaging - Nils Lid Hjort,Gerda Claeskens

Anglų
2014-05-14
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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 ... Visas aprašymas

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Aprašymas

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.

Daugiau informacijos

Autorius Nils Lid Hjort, Gerda Claeskens
Leidėjas Cambridge University Press
Išleidimo metai 2014
Viršelio tipas Kieti viršeliai
EAN 9780521852258
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155,13 € 206,84 €