Artículo
Multvariate Principal Components for Functional Data
Fecha de publicación:
09/2011
Editorial:
Elsevier Science
Revista:
Computational Statistics and Data Analysis
ISSN:
0167-9473
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
A principal component method for multivariate functional data is proposed. Data can be arranged in a matrix whose elements are functions so that for each individual a vector of p functions is observed. This set of p curves is reduced to a small number of transformed functions, retaining as much information as possible. The criterion to measure the information loss is the integrated variance. Under mild regular conditions, it is proved that if the original functions are smooth this property is inherited by the principal components. A numerical procedure to obtain the smooth principal components is proposed and the goodness of the dimension reduction is assessed by two new measures of the proportion of explained variability. The method performs as expected in various controlled simulated data sets and provides interesting conclusions when it is applied to real data sets.
Palabras clave:
DIMENSION REDUCTION
,
EIGENVALUE FUNCTIONS
,
EXPLAINED VARIABILITY
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Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Berrendero, J. R.; Justel, Ana; Svarc, Marcela; Multvariate Principal Components for Functional Data; Elsevier Science; Computational Statistics and Data Analysis; 55; 9; 9-2011; 2619-2634
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