Publication:
Penalized function-on-function partial leastsquares regression

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dc.affiliation.dptoUC3M. Departamento de EstadĂ­sticaes
dc.contributor.authorHernandez Roig, Harold Antonio
dc.contributor.authorAguilera Morillo, MarĂ­a del Carmen
dc.contributor.authorAguilera, Ana M.
dc.contributor.authorPreda, Cristian
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de EstadĂ­sticaes
dc.date.accessioned2023-07-05T17:23:48Z
dc.date.available2023-07-05T17:23:48Z
dc.date.issued2023-07-05
dc.description.abstractThis paper deals with the "function-on-function'" or "fully functional" linear regression problem. We address the problem by proposing a novel penalized Function-on-Function Partial Least-Squares (pFFPLS) approach that imposes smoothness on the PLS weights. Our proposal introduces an appropriate finite-dimensional functional space with an associated set of bases on which to represent the data and controls smoothness with a roughness penalty operator. Penalizing the PLS weights imposes smoothness on the resulting coefficient function, improving its interpretability. In a simulation study, we demonstrate the advantages of pFFPLS compared to non-penalized FFPLS. Our comparisons indicate a higher accuracy of pFFPLS when predicting the response and estimating the true coefficient function from which the data were generated. We also illustrate the advantages of our proposal with two case studies involving two well-known datasets from the functional data analysis literature. In the first one, we predict log precipitation curves from the yearly temperature profiles recorded in 35 weather stations in Canada. In the second case study, we predict the hip angle profiles during a gait cycle of children from their corresponding knee angle profiles.en
dc.identifier.issn2387-0303
dc.identifier.urihttps://hdl.handle.net/10016/37758
dc.identifier.uxxiDT/0000002080es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper Statistics and EconometricseN
dc.relation.ispartofseries23-07
dc.rightsAtribuciĂ³n-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadĂ­sticaes
dc.subject.otherFunctional Data Analysisen
dc.subject.otherPartial Least Squaresen
dc.subject.otherFunction-On-Function Regressionen
dc.subject.otherRoughness Penaltiesen
dc.titlePenalized function-on-function partial leastsquares regressionen
dc.typeworking paper*
dspace.entity.typePublication
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