Model-based clustering for multivariate functional data

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...

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Veröffentlicht in:Computational statistics & data analysis Jg. 71; S. 92 - 106
Hauptverfasser: Jacques, Julien, Preda, Cristian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2014
Elsevier
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2012.12.004