Model-based clustering for multivariate functional data. (English) Zbl 1471.62096
Summary: 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.
MSC:
| 62-08 | Computational methods for problems pertaining to statistics |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62H25 | Factor analysis and principal components; correspondence analysis |
| 62R10 | Functional data analysis |
Keywords:
multivariate functional data; density approximation; model-based clustering; multivariate functional principal component analysis; EM-algorithmReferences:
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