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