# Structural components in functional data

## Détails

ID Serval

serval:BIB_411E6C9ED6CB

Type

**Article**: article d'un périodique ou d'un magazine.

Collection

Publications

Fonds

Titre

Structural components in functional data

Périodique

Computational Statistics and Data Analysis

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