Free Access
Issue
Genet. Sel. Evol.
Volume 38, Number 6, November-December 2006
Page(s) 583 - 600
DOI https://doi.org/10.1051/gse:2006023
Published online 28 November 2006
References of  Genet. Sel. Evol. 38 (2006) 583-600
  1. Atchley W.R., Zhu J., Developmental quantitative genetics, conditional epigenetic variability and growth in mice, Genetics 147 (1997) 765-776 [PubMed].
  2. Blasco A., Piles M., Varona L., A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits, Genet. Sel. Evol. 35 (2003) 21-41 [EDP Sciences] [CrossRef] [PubMed].
  3. Booth J.G., Hobert J.P., Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm, J. R. Statist. Soc. B. 61 (1999) 265-285 [CrossRef].
  4. Celeux G., Diebolt J., The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Comp. Statist. Quat. 2 (1985) 73-82.
  5. Davidian M., Giltinan D., Nonlinear models for repeated measurement data: an overview and update, J. Agric. Biol. Env. Satist. 4 (2003) 387-419.
  6. Delyon B., Lavielle M., Moulines E., Convergence of a stochastic approximation version of the EM algorithm, Ann. Statist. 27 (1999) 94-128 [CrossRef] [MathSciNet].
  7. Diggle P.J., Liang K.Y., Zeger S.L., Analysis of longitudinal data, Oxford University Press, Oxford, 1994.
  8. Foulley J.L., van Dyk D.A., The PX EM algorithm for fast fitting of Henderson's mixed model, Genet. Sel. Evol. 32 (2000) 143-163 [EDP Sciences] [CrossRef] [PubMed].
  9. Geyer C.J., On the convergence of Monte Carlo maximum likelihood calculations, J. R. Statist. Soc. B. 56 (1994) 261-274.
  10. Gilmour A.R., Thompson R., Cullis B.R., Welham S.J., ASREML Manual, New South Wales Department of Agriculture, Orange, Australia, 2002.
  11. Huisman A.E., Veerkamp R.F., van Arendonk J.A.M., Genetic parameters for various random regression models to describe weight data of pigs, J. Anim. Sci. 80 (2002) 575-582 [PubMed].
  12. Jaffrézic F., Thompson R., Hill W.G., Structured antedependence models for genetic analysis of multivariate repeated measures in quantitative traits, Genet. Res. 82 (2003) 55-65 [CrossRef] [PubMed].
  13. Jaffrézic F., Venot E., Laloë D., Vinet A., Renand G., Use of structured antedependence models for the genetic analysis of growth curves, J. Anim. Sci. 82 (2004) 3465-3473 [PubMed].
  14. Kuhn E., Lavielle M., Coupling a stochastic approximation version of EM with a MCMC procedure, ESAIM Prob. Statist. 8 (2004) 115-131.
  15. Kuhn E., Lavielle M., Maximum likelihood estimation in nonlinear mixed effects models, Comput. Statist. Data Anal. 49 (2005) 1020-1038 [CrossRef] [MathSciNet].
  16. Lavielle M., Monolix User Guide Manual, 2005, http://www.math.u-psud.fr/~lavielle/monolix/logiciels.
  17. Levine R.A., Casella G., Implementations of the Monte Carle EM algorithm, J. Comp. Graph. Statist. 10 (2001) 1-18.
  18. Lindstrom M.J., Bates D.M., Nonlinear mixed-effects models for repeated measures data, Biometrics 46 (1990) 673-687 [PubMed] [MathSciNet].
  19. Louis T.A., Finding the observed information matrix when using the EM algorithm, J. R. Statist. Soc. B. 44 (1982) 226-233.
  20. Ma C.-X., Casella G., Wu R.L., Functional mapping of quantitative trait loci underlying the character process: a theoretical framework, Genetics 161 (2002) 1751-1762 [PubMed].
  21. McCulloch C.E., Maximum likelihood algorithms for generalized linear mixed models, J. Am. Statist. Assoc. 92 (1997) 162-170.
  22. Meng X.L., van Dyk D.A., Fast EM-type implementations for mixed effects models, J. R. Statist. Soc. B. 60 (1998) 559-578 [CrossRef].
  23. Mialon M.M., Renand G., Krauss D., Ménissier F., Variability of the postpartum recovery of sexual activity of Charolais cows, Livest. Prod. Sci. 69 (2001) 217-226 [CrossRef].
  24. Mignon-Grasteau S., Piles M., Varona L., de Rochambeau H., Poivey J.P., Blasco A., Beaumont C., Genetic analysis of growth curve parameters for male and female chickens resulting from selection on shape of growth curve, J. Anim. Sci. 78 (2000) 2515-2524 [PubMed].
  25. Nunez-Anton V., Zimmerman D.L., Modeling non-stationary longitudinal data, Biometrics 56 (2000) 699-705 [CrossRef] [PubMed].
  26. Pletcher S.D., Jaffrézic F., Generalized character process models: estimating the genetic basis of traits that cannot be observed and that change with age or environmental conditions, Biometrics 58 (2002) 157-162 [CrossRef] [PubMed] [MathSciNet].
  27. Spiegelhalter D.J., Thomas A., Best N.G., WinBUGS Version 1.4 User Manual, Cambridge: Medical Research Council Biostatistics Unit, http://www.mrc-bsu.cam.ac.uk/bugs, 2004.
  28. Wei G.C.G., Tanner M.A., A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms, J. Am. Statist. Assoc. 85 (1990) 699-704.