PLS Regression with Functional Predictor and Missing Data

Cristian Preda; Gilbert Saporta; Ben Hadj Mbarek

Conference papers

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

2 Université de Lille, Sciences et Technologies

3 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage

CEDRIC - Centre d'études et de recherche en informatique et communications

4 Institut Supérieur de Gestion Sousse

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.

Keywords : functional data missing data PLS functional regression models

Document type :

Conference papers

Domain :

Mathematics [math] / Statistics [math.ST]

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Submitted on : Monday, March 23, 2020 - 6:57:36 PM
Last modification on : Friday, November 27, 2020 - 2:18:03 PM

PLS Regression with Functional Predictor and Missing Data

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