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Title: Multivariate functional clustering and its application to typhoon data Authors:  Toshihiro Misumi - Yokohama City University (Japan) [presenting]
Hidetoshi Matsui - Shiga University (Japan)
Sadanori Konishi - Chuo University (Japan)
Abstract: A multivariate nonlinear mixed effects model is proposed for clustering multiple longitudinal data. The advantages of the nonlinear mixed effects model are that it is easy to handle unbalanced data which are highly occurred in the longitudinal study, and that it can account for correlations at a given time point among longitudinal characteristics. The joint modeling for multivariate longitudinal data, however, requires high computational cost because numerous parameters are included in the model. To overcome this issue, we perform a pairwise fitting procedure based on a pseudo-likelihood function. Unknown parameters included in each bivariate model are estimated by the maximum likelihood method along with the EM algorithm, and then the numbers of basis functions included in the model are selected by model selection criteria. After estimating the model, a non-hierarchical clustering algorithm by self-organizing maps is implemented to predicted coefficient vectors of individual specific random effect functions. We present the results of application of the proposed method to the analysis of data of typhoon occurred between 2000-2017 in Asia.