Asymptotic Properties and Computation of Maximum Likelihood Estimates in the Mixed Model of the Analysis of Variance

Abstract

The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood.

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Document Details

Document Type
Technical Report
Publication Date
Nov 21, 1973
Accession Number
AD0775399

Entities

People

  • John James Miller

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Asymptotic Normality
  • Computational Science
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Science
  • Experimental Design
  • Information Science
  • Maximum Likelihood Estimation
  • Numerical Analysis
  • Personality
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference

Fields of Study

  • Mathematics

Readers

  • Statistical inference.