Structural components in functional data

Détails

ID Serval
serval:BIB_411E6C9ED6CB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Structural components in functional data
Périodique
Computational Statistics and Data Analysis
Auteur(s)
Park Juhyun, Gasser Theo, Rousson Valentin
ISSN
0167-9473
Statut éditorial
Publié
Date de publication
2009
Peer-reviewed
Oui
Volume
53
Numéro
9
Pages
3452-3465
Langue
anglais
Résumé
Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L-2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.
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Création de la notice
06/02/2010 19:22
Dernière modification de la notice
03/03/2018 16:30
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