PLS Regression with Functional Predictor and Missing Data

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.