Improved Estimation of Variance Components in Mixed Models

Abstract

Taking Albert's (1976) formulation of a mixed model Analysis of variance, we consider improved estimation of the variance components for balanced designs under squared error loss. Two approaches are presented. One extends the ideas of Stein (1964). The other is developed from the fact that variance components can be expressed as linear combinations of chi-scale parameters. Encouraging simulation results are presented. Keywords: Linear regression analysis, Chi square scale parameters.

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Document Details

Document Type
Technical Report
Publication Date
Aug 30, 1990
Accession Number
ADA226854

Entities

People

  • Alan E. Gelfand
  • Dipak K. Dey

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Science
  • Estimators
  • Experimental Design
  • Information Science
  • Linear Regression Analysis
  • Military Research
  • Probability
  • Regression Analysis
  • Simulations
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.