Multivariate longitudinal data arisen in medical studies often exhibit complex features such
as censored responses, intermittent missing values, and atypical or outlying observations …

Multivariate nonlinear mixed-effects models (MNLMMs) have become a promising tool for
analyzing multi-outcome longitudinal data following nonlinear trajectory patterns. However …

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for
longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal …

A class of multivariate linear models under the longitudinal setting, in which unobserved
heterogeneity may evolve over time, is introduced. A latent structure is considered to model …

The use of the first two moments of the truncated multivariate Student-t distribution has
attracted increasing attention from a wide range of applications. This paper develops …

The analysis of multivariate longitudinal data could encounter some complications due to
censorship induced by detection limits of the assay and nonresponse occurring when …

We consider the analysis of continuous repeated measurement outcomes that are collected
longitudinally. A standard framework for analysing data of this kind is a linear Gaussian …

In longitudinal studies, repeated measures are collected over time and hence they tend to
be serially correlated. These studies are commonly analyzed using linear mixed models …

Finite mixture models have been widely used for the modeling and analysis of data from a
heterogeneous population. Moreover, data of this kind can be subject to some upper and/or …

Mixtures of factor analyzers (MFA) provide a promising tool for modeling and clustering high-
dimensional data that contain an overwhelmingly large number of attributes measured on …