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A Bayesian Analysis of the Minimum AIC Procedure

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Selected Papers of Hirotugu Akaike

Part of the book series: Springer Series in Statistics ((PSS))

Summary

By using a simple example a minimax type optimality of the minimum AIC procedure for the selection of models is demonstrated.

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References

  1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle, 2nd International Symposium of Information Theory, B. N. Petrov and F. Csaki, eds., Akademiai Kiado, Budapest, 267–281.

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  2. Akaike, H. (1974). A new look at the statistical model identification, IEEE Trans. Automat. Contr., AC-19, 716–723.

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  3. Akaike, H. (1977). On entropy maximization principle, Applications of Statistics, P. R. Krishnaiah, ed., North-Holland, Amsterdam, 27–41.

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  4. Schwarz, G. (1976). Estimating the dimension of a model, Ann. Statist., 6, 461–464.

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Authors
  1. Hirotugu Akaike
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Editor information

Editors and Affiliations

  1. Department of Statistics, Texas A&M University, 77843, College Station, TX, USA

    Emanuel Parzen

  2. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, 106, Minato-ku Tokyo, Japan

    Kunio Tanabe  & Genshiro Kitagawa  & 

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© 1998 Springer Science+Business Media New York

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Akaike, H. (1998). A Bayesian Analysis of the Minimum AIC Procedure. In: Parzen, E., Tanabe, K., Kitagawa, G. (eds) Selected Papers of Hirotugu Akaike. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1694-0_21

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  • DOI: https://doi.org/10.1007/978-1-4612-1694-0_21

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7248-9

  • Online ISBN: 978-1-4612-1694-0

  • eBook Packages: Springer Book Archive

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