Model selection and model averaging /

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 suitable model is central to all statistical work with...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Claeskens, Gerda, 1973- (Autor), Hjort, Nils Lid (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge ; New York : Cambridge University Press, 2008.
Colección:Cambridge series on statistical and probabilistic mathematics.
Temas:
Mathematical models > Research.
Mathematical statistics > Research.
Bayesian statistical decision theory.
Modèles mathématiques > Recherche.
Statistique mathématique > Recherche.
Théorie de la décision bayésienne.
MATHEMATICS > Probability & Statistics > General.
Statistisches Modell
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • Further topics.

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