Information Criteria and Statistical Modeling

Winner of the 2009 Japan Statistical Association Publication Prize. The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been repor...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Konishi, Sadanori (Autor), Kitagawa, Genshiro (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Springer Series in Statistics,
Temas:
Statistics .
Mathematical models.
Coding theory.
Information theory.
Data mining.
Computer science-Mathematics.
Mathematical statistics.
Computer simulation.
Statistical Theory and Methods.
Mathematical Modeling and Industrial Mathematics.
Coding and Information Theory.
Data Mining and Knowledge Discovery.
Probability and Statistics in Computer Science.
Computer Modelling.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Concept of Statistical Modeling
  • Statistical Models
  • Information Criterion
  • Statistical Modeling by AIC
  • Generalized Information Criterion (GIC)
  • Statistical Modeling by GIC
  • Theoretical Development and Asymptotic Properties of the GIC
  • Bootstrap Information Criterion
  • Bayesian Information Criteria
  • Various Model Evaluation Criteria.

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