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Generalised Information Criteria in Model Selection
Sadanori Konishi and Genshiro Kitagawa
Vol. 83, No. 4 (Dec., 1996), pp. 875-890
Stable URL: http://www.jstor.org/stable/2337290
Page Count: 16
You can always find the topics here!Topics: Estimation bias, Statistical models, Maximum likelihood estimation, Sampling bias, Statism, Simulations, Statistical estimation, Statistical variance, Parametric models, Maximum likelihood estimators
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The problem of evaluating the goodness of statistical models is investigated from an information-theoretic point of view. Information criteria are proposed for evaluating models constructed by various estimation procedures when the specified family of probability distributions does not contain the distribution generating the data. The proposed criteria are applied to the evaluation of models estimated by maximum likelihood, robust, penalised likelihood, Bayes procedures, etc. We also discuss the use of the bootstrap in model evaluation problems and present a variance reduction technique in the bootstrap simulation.
Biometrika © 1996 Biometrika Trust