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PLS Regression with Functional Predictor and Missing Data

Cristian Preda 1, 2 Gilbert Saporta 3 Ben Hadj Mbarek 4 
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
3 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : 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.
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Submitted on : Monday, March 23, 2020 - 6:57:36 PM
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  • HAL Id : hal-01125705, version 1



Cristian Preda, Gilbert Saporta, Ben Hadj Mbarek. PLS Regression with Functional Predictor and Missing Data. PLS'09,6th Int. Conf. on Partial Least Squares and Related Methods, Sep 2009, Pékin, China. pp.17-22. ⟨hal-01125705⟩



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