Information Criteria for Discriminating Among Alternative Regression Models
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In this paper, decision rules for discriminating among alternative regression models are proposed and mutually compared based on the Akaike Information Criterion as well as the Kullback-Leibler information Criterion (KLIC).Abstract:
Some decision rules for discriminating among alternative regression models are proposed and mutually compared. They are essentially based on the Akaike Information Criterion as well as the Kullback-Leibler Information Criterion (KLIC) : namely, the distance between a postulated model and the true unknown structure is measured by the KLIC. The proposed criteria combine the parsimony of parameters with the goodness of fit. Their relationships with conventional criteria are discussed in terms of a new concept of unbiasedness .read more
Citations
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References
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Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: 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.
Journal ArticleDOI
An Introduction to Bayesian Inference in Econometrics.
J. D. Sargan,Arnold Zellner +1 more