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Information Theory and an Extension of the Maximum Likelihood Principle

In this paper it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion. This observation shows an extension of the principle to provide answers to many practical problems of statistical model fitting.

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1. Institute of Statistical Mathematics, Japan

   Hirotogu Akaike

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  & 

© 1998 Springer Science+Business Media New York

Akaike, H. (1998). Information Theory and an Extension of the Maximum Likelihood Principle. 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_15

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• DOI: https://doi.org/10.1007/978-1-4612-1694-0_15

• 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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