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

Genetics Selection Evolution

Christèle Robert-Granié²  Florence Jaffrézic¹  Jean-Louis Foulley² 

[1] Institute of Cell, Animal and Population Biology The University of Edinburgh Edinburgh EH9 3JT, UK;Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, 78352 Jouy-en-Josas Cedex, France

关键词: longitudinal data;    random regression;    mixed models;    REML;    EM algorithm;   

Others  :  1094830
DOI  :  10.1186/1297-9686-32-2-129

 received in 1999-09-24, accepted in 1999-11-30,  发布年份 2000

【 摘 要 】

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.

【 授权许可】

   
2000 INRA, EDP Sciences