Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data

  • Florence JaffrézicEmail author
  • Ian MS White
  • Robin Thompson
Open Access


Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.


genetic longitudinal data analysis score test goodness-of-fit measure covariance structure 

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Copyright information

© INRA, EDP Sciences 2003

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Florence Jaffrézic
    • 1
    • 2
    Email author
  • Ian MS White
    • 1
  • Robin Thompson
    • 3
    • 4
  1. 1.Institute of Cell Animal and Population BiologyUniversity of EdinburghEdinburghUK
  2. 2.Station de génétique quantitative et appliquéeInstitut national de la recherche agronomiqueJouy-en-Josas CedexFrance
  3. 3.Rothamsted Experimental Station, IACRHarpendenUK
  4. 4.Roslin Institute (Edinburgh)Roslin, MidlothianUK

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