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

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.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1987
Accession Number
ADA186027

Entities

People

  • Ryuei Nishii

Organizations

  • University of Pittsburgh

Tags

DTIC Thesaurus Topics

  • Air Force
  • Asymptotic Normality
  • Consistency
  • Data Science
  • Estimators
  • Governments
  • Information Science
  • Law
  • Multivariate Analysis
  • National Governments
  • Observation
  • Probability
  • Scientific Research
  • Statistical Algorithms
  • Statistical Inference
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Statistical inference.