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

R. Nishii

doi:10.1016/0047-259X(88)90137-6

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

R. Nishii

Research output: Contribution to journal › Article › peer-review

86 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 language	English
Pages (from-to)	392-403
Number of pages	12
Journal	Journal of Multivariate Analysis
Volume	27
Issue number	2
DOIs	https://doi.org/10.1016/0047-259X(88)90137-6
Publication status	Published - Nov 1988
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Numerical Analysis
Statistics, Probability and Uncertainty

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10.1016/0047-259X(88)90137-6

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title = "Maximum likelihood principle and model selection when the true model is unspecified",

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.",

author = "R. Nishii",

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SN - 0047-259X

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

Abstract

All Science Journal Classification (ASJC) codes

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