Local linear regression for functional predictor and scalar response

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dc.contributor.author Baíllo, Amparo
dc.contributor.author Grané, Aurea
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2007-09-10T11:46:10Z
dc.date.available 2007-09-10T11:46:10Z
dc.date.issued 2007-08
dc.identifier.uri http://hdl.handle.net/10016/938
dc.description.abstract The aim of this work is to introduce a new nonparametric regression technique in the context of functional covariate and scalar response. We propose a local linear regression estimator and study its asymptotic behaviour. Its finite-sample performance is compared with a Nadayara-Watson type kernel regression estimator via a Monte Carlo study and the analysis of two real data sets. In all the scenarios considered, the local linear regression estimator performs better than the kernel one, in the sense that the mean squared prediction error and its standard deviation are lower.
dc.format.extent 431418 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 07-15
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Functional data
dc.subject.other Nonparametric smoothing
dc.subject.other Local linear regression
dc.subject.other Kernel regression
dc.subject.other Fourier expansion
dc.subject.other Cross-validation
dc.title Local linear regression for functional predictor and scalar response
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws076115
dc.affiliation.dpto UC3M. Departamento de Estadística
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