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

R. Nishii

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)392-403
Number of pages12
JournalJournal of Multivariate Analysis
Volume27
Issue number2
DOIs
Publication statusPublished - Nov 1988

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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