Estimation of Variance Components.

Abstract

The paper describes a number of methods for estimating variance components in a general linear model. Explicit expressions are obtained for locally minimum variance unbiased estimators with and without the invariance condition. The principle of MINQE is described and a series of MIQE estimators satisfying one or more of the conditions - unbiasedness, invariance, nonnegative definiteness - are derived. Corresponding to each MINQE estimator, an iterated MINQE (IMINQE) is defined. It is shown that the ML (maximum Likelihood) estimator is IMINQE satisfying the invarianc e condition and the RML (restricted maximum likelihood estimator) is IMINQE satisfying both the invariance and unbiasedness conditions. Some comments are made on the numerical alorithms for computing MINQ, ML and RML estimators. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1979
Accession Number
ADA074223

Entities

People

  • Calyampudi Radhakrishna Rao
  • Jurgen Kleffe

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Analysis Of Variance
  • Biometrics
  • Computations
  • Covariance
  • Data Analysis
  • Data Mining
  • Data Science
  • Estimators
  • Information Science
  • Maximum Likelihood Estimation
  • Regression Analysis
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Ballistic Missile Meteorology
  • Statistical inference.