Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6870298 | Computational Statistics & Data Analysis | 2014 | 15 Pages |
Abstract
The first model-based clustering algorithm for multivariate functional data is proposed. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal component scores, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into account the dependence among curves. Results on simulated and real datasets show the efficiency of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Julien Jacques, Cristian Preda,