A new look at statistical model identification.

Author(s)
Akaike, H.
Year
Abstract

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 estimated of the parameters which give the minimum of AIC defined by AIC = (-2)log (maxumim 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 procedures. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.

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Publication

Library number
950652 ST [electronic version only]
Source

IEEE Transactions on Automatic Control, Vol. 19 (1974), No. 6 (December), p. 716-723, 45 ref.

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