Maximum likelihood principle and model selection when the true model is unspecified

R. Nishii

Journal of Multivariate Analysis, 1988, vol. 27, issue 2, 392-403

Abstract: Suppose that independent observations come from an unspecified unknown distribution. Then we consider the maximum likelihood based on a specified parametric family which provides a good approximation of the true distribution. We examine the asymptotic properties of the maximum likelihood estimate and of the maximum likelihood. These results will be applied to the model selection problem.

Keywords: AIC; BIC; consistency; law; of; iterated; logarithm; MLE; regularity; conditions (search for similar items in EconPapers)
Date: 1988
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Citations: View citations in EconPapers (42)

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