A generalization of functional clustering for discrete multivariate longitudinal data. (November 2020)

• Record Type: Journal Article
• Authors: Lim, Yaeji; Cheung, Ying Kuen; Oh, Hee-Seok
• Is Part Of: Statistical methods in medical research. Volume 29:Number 11(2020)
• Page Start: 3205
• Page End: 3217
• Publication Date: 2020-11
• Subjects: Binomial data -- functional clustering -- latent Gaussian process -- model-based clustering -- multivariate functional principal component analysis -- Poisson data; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220921912
• Languages: English
• ISSNs: 0962-2802

Abstract

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.