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Estimation in Covariance Components Models

A. P. Dempster^{a b}, Donald B. Rubin^{c d}, Robert K. Tsutakawa^{e f}

^aHarvard University
^bDepartment of Statistics, Harvard University, Cambridge, MA 02138 USA
^cEducational Testing Service
^dPrinceton NJ USA
^eUniversity of Missouri
^fDepartment of Statistics, University of Missouri, Columbia, MO, 65211, USA

Journal of the American Statistical Association1981-06-01 452 citations24 references

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Abstract

Estimation techniques for linear covariance components models are developed and illustrated with special emphasis on explaining computational processes. The estimation of fixed and random effects when the variances and covariances are known is presented in Bayesian terms, Point estimates of the unknown variances and covariances are computed using the EM algorithm for maximum likelihood estimation from incomplete data. The techniques are illustrated with data on law schools, field mice, and professional football teams.

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Estimation in Covariance Components Models

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