Balanced longitudinal data clustering with a copula kernel mixture model
Xi Zhang, Orla A. Murphy, Paul D. McNicholas · Canadian Journal of Statistics · 2025
Abstract Many common clustering methods cannot be used for clustering balanced multivariate longitudinal data in cases where the covariance of variables is a function of the time points. In this article, a copula kernel mixture model (CKMM) is proposed for clustering data of this type. The CKMM is a finite mixture model that decomposes each mixture component's joint density function into a copula and marginal distribution functions. In this decomposition, the Gaussian copula is used due to its mathematical tractability and Gaussian kernel functions are used to estimate the marginal distributions. A generalized expectation‐maximization algorithm is used to estimate the model parameters. The performance of the proposed model is assessed in a simulation study and on two real datasets. The proposed model is shown to have effective performance in comparison with standard methods, such as ‐means with dynamic time warping clustering, latent growth models and functional high‐dimensional data clustering.