Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Profiled deviance for the multivariate linear mixed-effects model fitting

Abstract : This paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the marginal residual terms are assumed uncorrelated and homoscedastic with possibly different standard deviations. The random effects covariance matrix is Cholesky factorized to directly estimate the variance components of these random effects. This strategy enables a consistent estimate of the random effects covariance matrix which, generally, has a poor estimate when it is grossly (or directly) estimated, using the estimating methods such as the EM algorithm. By using simulated data sets, we compare the estimates based on the present method with the EM algorithm-based estimates. We provide an illustration by using the real-life data concerning the study of the child's immune against malaria in Benin (West Africa).
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [28 references]  Display  Hide  Download
Contributor : Eric Adjakossa <>
Submitted on : Tuesday, May 2, 2017 - 12:04:25 PM
Last modification on : Saturday, March 28, 2020 - 2:20:50 AM
Document(s) archivé(s) le : Thursday, August 3, 2017 - 12:55:21 PM


Files produced by the author(s)


  • HAL Id : hal-01494186, version 2
  • ARXIV : 1703.08045


Eric Adjakossa, Grégory Nuel. Profiled deviance for the multivariate linear mixed-effects model fitting. 2017. ⟨hal-01494186v2⟩



Record views


Files downloads