Lasso variable selection in functional regression

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dc.contributor.author Mingotti, Nicola
dc.contributor.author Lillo Rodríguez, Rosa Elvira
dc.contributor.author Romo Urroz, Juan
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2013-05-10T12:41:27Z
dc.date.available 2013-05-10T12:41:27Z
dc.date.issued 2013-05
dc.identifier.uri http://hdl.handle.net/10016/16959
dc.description.abstract Functional Regression has been an active subject of research in the last two decades but still lacks a secure variable selection methodology. Lasso is a well known effective technique for parameters shrinkage and variable selection in regression problems. In this work we generalize the Lasso technique to select variables in the functional regression framework and show it performs well. In particular, we focus on the case of functional regression with scalar regressors and functional response. Reduce the associated functional optimization problem to a convex optimization on scalars. Find its solutions and stress their interpretability. We apply the technique to simulated data sets as well as to a new real data set: car velocity functions in low speed car accidents, a frequent cause of whiplash injuries. By “Functional Lasso” we discover which car characteristics influence more car speed and which can be considered not relevant
dc.description.sponsorship This research was supported in part by Spanish Ministry of Education and Science grants MEC 2009/00035/001, ECO2011-25706 and SEJ2007
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 13-13
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Norm one penalization
dc.subject.other Variable selection
dc.subject.other Algebraic re-duction
dc.subject.other Convex optimization
dc.subject.other Computer algebra
dc.title Lasso variable selection in functional regression
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2011-25706
dc.type.version submitedVersion
dc.identifier.repec ws131413
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