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

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  • Nishii, R.

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.

Suggested Citation

  • Nishii, R., 1988. "Maximum likelihood principle and model selection when the true model is unspecified," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 392-403, November.
  • Handle: RePEc:eee:jmvana:v:27:y:1988:i:2:p:392-403
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