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

  • Jean-Louis Foulley
  • Florence Jaffrézic
  • Christèle Robert-Granié
Open Access
Research

Abstract

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.

Keywords

EM algorithm REML mixed models random regression longitudinal data 

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Copyright information

© INRA, EDP Sciences 2000

Authors and Affiliations

  • Jean-Louis Foulley
    • 1
  • Florence Jaffrézic
    • 2
  • Christèle Robert-Granié
    • 1
  1. 1.Station de génétique quantitative et appliquéeInstitut national de la recherche agronomiqueJouy-en-Josas CedexFrance
  2. 2.Institute of Cell, Animal and Population BiologyThe University of EdinburghEdinburghUK

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