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
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.
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⟩