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[PSH09] PLS Regression with Functional Predictor and Missing Data
Conférence Internationale avec comité de lecture : PLS'09,6th Int. Conf. on Partial Least Squares and Related Methods, Pékin, September 2009, pp.17-22, Pékin, Chine,
motcle:
Résumé:
Time-average approximation and principal component analysis of the stochastic process
underlying the functional data are the main ingredients 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.
Keywords: functional data, missing data, PLS, functional regression models.
Equipe:
msdma
BibTeX
| @inproceedings { | |||
| PSH09, | |||
| title | = | "{PLS Regression with Functional Predictor and Missing Data}", | |
| author | = | " C. Preda and G. Saporta and B. Hadj Mbarek ", | |
| booktitle | = | "{PLS'09,6th Int. Conf. on Partial Least Squares and Related Methods, Pékin}", | |
| year | = | 2009, | |
| month | = | "September", | |
| pages | = | "17-22", | |
| address | = | "Pékin, Chine", | |
| } | |||
