Estimation in multivariate t linear mixed models for multiple longitudinal data

Wan Lun Wang, Tsai Hung Fan

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


The multivariate linear mixed model (MLMM) is a frequently used tool for a joint analysis of more than one series of longitudinal data. Motivated by a concern of sensitivity to potential outliers or data with longer-than-normal tails and possible serial correlation, we develop a robust generalization of the MLMM that is constructed by using the multivariate t distribution and a parsimonious AR(p) dependence structure for the within-subject errors. A score test for the inspection of autocorrelation among within-subject errors is derived. A hybrid ECME-scoring procedure is developed for computing the maximum likelihood estimates with standard errors as a by-product. The methodology is illustrated through an application to a set of AIDS data and several simulation studies.

Original languageEnglish
Pages (from-to)1857-1880
Number of pages24
JournalStatistica Sinica
Issue number4
StatePublished - Oct 2011


  • AR(p)
  • ECME algorithm
  • Outliers
  • Random effects
  • Score test


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