With the advance of techniques, more and more complicated data are extracted and recorded. In this paper, functional regression models with a scalar response and multiple predictive curves are considered. We transform the functional regression models to multiple linear regression models by using...

Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the...

Principal component analysis (PCA) is one of the key techniques in functional data analysis. One important feature of functional PCA is that there is a need for smoothing or regularizing of the estimated principal component curves. Silverman's method for smoothed functional principal component...