Department of Applied Statistics, Chung-Ang University, Seoul, Republic of Korea.
Department of Biostatistics, Columbia University, New York, USA.
Stat Methods Med Res. 2020 Nov;29(11):3205-3217. doi: 10.1177/0962280220921912. Epub 2020 May 5.
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.
本文提出了一种新的基于模型的广义功能聚类方法，用于离散纵向数据，如多元二项式和泊松分布数据。为此，我们提出了一种基于多变量函数主成分分析（MFPCA）的聚类方法，用于潜在的多变量高斯过程，而不是直接对原始功能数据进行聚类。本研究的主要贡献有两个方面：用潜在的多变量高斯过程对离散纵向数据进行建模，以及开发一种基于 MFPCA 和潜在的多变量高斯过程的聚类算法。包括真实数据分析和模拟研究在内的数值实验证明了所提出方法的有希望的经验性质。
