A New Look at the Statistical Model Identification

  • Hirotugu Akaike
Part of the Springer Series in Statistics book series (SSS)


The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (−2)log- (maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.


Time Series Analysis Hankel Matrix Classical Maximum Likelihood Gaussian Process Model Final Prediction Error 
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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Copyright information

© Springer Science+Business Media New York 1974

Authors and Affiliations

  • Hirotugu Akaike
    • 1
  1. 1.The Institute of Statistical MathematicsMinato-ku, TokyoJapan

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