Multivariate t semiparametric mixed-effects model for longitudinal data with multiple characteristics

M. Taavoni, M. Arashi, Wan Lun Wang, Tsung I. Lin

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Semiparametric mixed-effects models (SMM) have received increasing attention in recent years because of the greater flexibility in analysing longitudinal trajectories. However, the normality assumption of SMM may be unrealistic when outliers occur in the data. This paper presents a semiparametric extension of the multivariate t linear mixed-effects model (MtLMM), called the multivariate t semiparametric mixed model (MtSMM). To be specific, the MtSMM incorporates a parametric linear function related to the fixed covariate effects and random effects which have a joint multivariate t distribution together with an arbitrary nonparametric smooth function to capture the unexpected patterns. A computationally analytical EM-based algorithm is developed for carrying out maximum likelihood estimation of the MtSMM. Simulation studies and a real example concerning the analysis of PBCseq data are used to investigate the empirical behaviour of the proposed methodology.

Original languageEnglish
Pages (from-to)260-281
Number of pages22
JournalJournal of Statistical Computation and Simulation
Issue number2
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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