TY - JOUR
T1 - Multivariate t semiparametric mixed-effects model for longitudinal data with multiple characteristics
AU - Taavoni, M.
AU - Arashi, M.
AU - Wang, Wan Lun
AU - Lin, Tsung I.
N1 - Funding Information:
The authors would like to thank the AE and two anonymous referees for their insightful comments which substantially improve the paper. W.L. Wang and T.I. Lin would like to acknowledge the support of the Ministry of Science and Technology of Taiwan under grant numbers MOST 107-2628-M-035-001-MY3 and MOST 107-2118-M-005-002-MY2, respectively.
Funding Information:
W. L. Wang and T. I. Lin would like to acknowledge the support of the Ministry of Science and Technology of Taiwan under [grant numbers MOST 107-2628-M-035-001-MY3 and MOST107-2118-M-005-002-MY2]. The authors would like to thank the AE and two anonymous referees for their insightful comments which substantially improve the paper. W.L. Wang and T.I. Lin would like to acknowledge the support of the Ministry of Science and Technology of Taiwan under grant numbers MOST 107-2628-M-035-001-MY3 and MOST 107-2118-M-005-002-MY2, respectively.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1080/00949655.2020.1812608
DO - 10.1080/00949655.2020.1812608
M3 - Article
AN - SCOPUS:85090089427
SN - 0094-9655
VL - 91
SP - 260
EP - 281
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 2
ER -