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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Vydáno v:	Computational statistics & data analysis Ročník 71; s. 92 - 106
Hlavní autoři:	Jacques, Julien, Preda, Cristian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.03.2014 Elsevier
Témata:	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
On-line přístup:	Získat plný text
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0167-9473 1872-7352
DOI:	10.1016/j.csda.2012.12.004