|
 
|
[PSH10a] The NIPALS Algorithm for Missing Functional Data
Revue Nationale avec comité de lecture : Journal Revue Roumaine de Mathématiques Pures et Appliquées, vol. 55(4), pp. 315-326, 2010Mots clés: functional data, missing data, principal components, partial least squares, functional regression models
Résumé:
Time-average approximation and principal component analysis of the stochastic
process underlying the functional data are the main tools for adapting NIPALS
algorithm to estimate missing data in the functional context. The influence of the
amount of missing data in the estimation of linear regression models is studied
using the PLS method. A simulation study illustrates our methodology.
Equipe:
msdma
BibTeX
| @article { | |||
| PSH10a, | |||
| title | = | "{The NIPALS Algorithm for Missing Functional Data}", | |
| author | = | "C. Preda and G. Saporta and M. Hadj Mbarek", | |
| journal | = | "Revue Roumaine de Mathématiques Pures et Appliquées", | |
| year | = | 2010, | |
| volume | = | 55, | |
| number | = | 4, | |
| pages | = | "315-326", | |
| } | |||
