Model-based clustering for multivariate functional data

• Published in: Computational statistics & data analysis Vol. 71; pp. 92 - 106
• Main Authors: Jacques, Julien, Preda, Cristian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2014; Elsevier
• Subjects: Algorithms; data collection; Density approximation; EM-algorithm; Mathematics; Model-based clustering; Multivariate functional data; Multivariate functional principal component analysis; principal component analysis; Statistics; Statistics Theory; Multivariate functional principal component analysis; Multivariate functional data; Density approximation; EM-algorithm; Model-based clustering; multivariate functional principal component analysis; model-based clustering; EM algorithm; density approximation
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2012.12.004

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.