@misc{10481/72907,
year = {2016},
month = {3},
url = {http://hdl.handle.net/10481/72907},
abstract = {Least squares estimation of the functional linear regression model with scalar response is an ill-posed
problem due to the infinite dimension of the functional predictor. Dimension reduction approaches as principal component regression or partial least squares regression are proposed and widely used in applications.
In both cases the interpretation of the model could be difficult because of the roughness of the coefficient
regression function. In this paper, two penalized estimations of this model based on modifying the partial
least squares criterion with roughness penalties for the weight functions are proposed. One introduces the
penalty in the definition of the norm in the functional space, and the other one in the cross-covariance operator. A simulation study and several applications on real data show the efficiency of the penalized approaches
with respect to the non-penalized ones.},
organization = {Project P11-FQM-8068 from Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía, Spain},
organization = {Projects MTM2013-47929-P and MTM 2011-28285-C02-C2 from Secretaría de Estado Investigación, Desarrollo e Innovación, Ministerio de Economía y Competitividad, Spain, and by Fondo Europeo de Desarrollo Regional (FEDER)},
publisher = {Elsevier},
keywords = {PLS},
keywords = {Functional data},
keywords = {Penalized splines},
keywords = {Basis representation},
title = {Penalized versions of functional PLS regression},
doi = {https://doi.org/10.1016/j.chemolab.2016.03.013},
author = {Aguilera Del Pino, Ana María and Aguilera Morillo, María del Carmen and Preda, C},
}