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Genetics Selection Evolution

Open Access

EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

  • Jean-Louis Foulley1Email author,
  • Florence Jaffrézic2 and
  • Christèle Robert-Granié1
Genetics Selection Evolution200032:129

Received: 24 September 1999

Accepted: 30 November 1999

Published: 15 March 2000


This paper presents procedures for implementing the EM algorithm to compute REML estimates of variance covariance components in Gaussian mixed models for longitudinal data analysis. The class of models considered includes random coefficient factors, stationary time processes and measurement errors. The EM algorithm allows separation of the computations pertaining to parameters involved in the random coefficient factors from those pertaining to the time processes and errors. The procedures are illustrated with Pothoff and Roy's data example on growth measurements taken on 11 girls and 16 boys at four ages. Several variants and extensions are discussed.


EM algorithmREMLmixed modelsrandom regressionlongitudinal data

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Authors’ Affiliations

Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, Jouy-en-Josas Cedex, France
Institute of Cell, Animal and Population Biology, The University of Edinburgh, Edinburgh, UK


© INRA, EDP Sciences 2000