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A generalization of functional clustering for discrete multivariate longitudinal data
Cited 5 time in
Web of Science
Cited 6 time in Scopus
- Authors
- Issue Date
- 2020-11
- Publisher
- SAGE Publications
- Citation
- Statistical Methods in Medical Research, Vol.29 No.11, pp.3205-3217
- 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.
- ISSN
- 0962-2802
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