PLS Regression with Functional Predictor ...
Document type :
Communication dans un congrès avec actes
Title :
PLS Regression with Functional Predictor and Missing Data
Author(s) :
Preda, Cristian [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Université de Lille, Sciences et Technologies
Saporta, Gilbert [Auteur]
CEDRIC. Méthodes statistiques de data-mining et apprentissage [CEDRIC - MSDMA]
Hadj Mbarek, Ben [Auteur]
Institut Supérieur de Gestion Sousse
MOdel for Data Analysis and Learning [MODAL]
Université de Lille, Sciences et Technologies
Saporta, Gilbert [Auteur]
CEDRIC. Méthodes statistiques de data-mining et apprentissage [CEDRIC - MSDMA]
Hadj Mbarek, Ben [Auteur]
Institut Supérieur de Gestion Sousse
Conference title :
PLS'09,6th Int. Conf. on Partial Least Squares and Related Methods
City :
Pékin
Country :
Chine
Start date of the conference :
2009-09-04
Publication date :
2009-09-04
English keyword(s) :
functional data
missing data
PLS
functional regression models
missing data
PLS
functional regression models
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
Time-average approximation and principal component analysis of the stochastic processunderlying the functional data are the main ingredients for adapting NIPALS algorithm to estimate missingdata in the functional context. ...
Show more >Time-average approximation and principal component analysis of the stochastic processunderlying the functional data are the main ingredients for adapting NIPALS algorithm to estimate missingdata in the functional context. The influence of the amount of missing data in the estimation of linearregression models is studied using the PLS method. A simulation study illustrates our methodology.Keywords: functional data, missing data, PLS, functional regression models.Show less >
Show more >Time-average approximation and principal component analysis of the stochastic processunderlying the functional data are the main ingredients for adapting NIPALS algorithm to estimate missingdata in the functional context. The influence of the amount of missing data in the estimation of linearregression models is studied using the PLS method. A simulation study illustrates our methodology.Keywords: functional data, missing data, PLS, functional regression models.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Non spécifiée
Popular science :
Non
Collections :
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