Model-based clustering for multivariate functional data

Julien Jacques and Cristian Preda

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 92-106

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.

Keywords: Multivariate functional data; Density approximation; Model-based clustering; Multivariate functional principal component analysis; EM-algorithm (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (54)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:92-106

DOI: 10.1016/j.csda.2012.12.004

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