“Information Theory and an Extension of the Maximum Likelihood Principle”, 1998 (; similar):
[From Selected Papers of Hirotugu Akaike, pg199–213; Originally published in +Proceeding of the Second International Symposium on Information Theory+, B.N. Petrov and F. Caski, eds., Akademiai Kiado, Budapest, 1973, 267–281]
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]