Accession Number : ADA186027

Title :   Maximum Likelihood Principle and Model Selection when the True Model is Unspecified.

Descriptive Note : Technical rept.,

Corporate Author : PITTSBURGH UNIV PA CENTER FOR MULTIVARIATE ANALYSIS

Personal Author(s) : Nishii, Ryuei

PDF Url : ADA186027

Report Date : Feb 1987

Pagination or Media Count : 18

Abstract : Suppose independent observations come from an unspecified distribution. Then we consider the maximum likelihood based on a specified parametric family by which we can approximate the true distribution well. We examine the asymptotic properties of the quasi-maximum likelihood estimate and of the quasi-maximum likelihood. These results will be applied to model selection problem. Keywords: AIC, BIC, Consistency. Law of iterated logarithm MLE, Regularity conditions.

Descriptors :   *MAXIMUM LIKELIHOOD ESTIMATION, DISTRIBUTION, MODELS, SELECTION, ASYMPTOTIC SERIES, DISTRIBUTION, MAXIMUM LIKELIHOOD ESTIMATION, SELECTION, DENSITY, MATHEMATICAL MODELS, PARAMETERS, ITERATIONS, ASYMPTOTIC NORMALITY, MULTIVARIATE ANALYSIS

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE