Extending growth mixture models using continuous non-elliptical distributions

Growth mixture models (GMMs) incorporate both conventional random effects growth modelling and latent trajectory classes as in finite mixture modelling; therefore, they offer a way to handle the unobserved heterogeneity between subjects in their development. GMMs with Gaussian random effects dominate the literature. However, when data are asymmetric and/or have heavier tails, more than one latent class is required to capture the observed variable distribution. Therefore, a GMM with continuous non-elliptical distributions is proposed to capture skewness and heavier tails.