Time-average approximation and principal component analysis of the stochastic process underlying the functional data are the main tools 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.