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Information Theory and an Extension of the Maximum Likelihood Principle

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Abstract

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.

Keywords

  • Autoregressive Model
  • Final Prediction Error
  • Maximum Likelihood Principle
  • Statistical Model Identification
  • Statistical Decision Function

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

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