Prediction Bands for Functional Data Based on Depth Measures

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Mostrar el registro sencillo del ítem Jiménez Recaredo, Raúl José Elías Fernández, Antonio
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística 2017-06-02T08:19:30Z 2017-06-02T08:19:30Z 2017-05
dc.identifier.issn 2387-0303
dc.description.abstract We propose a new methodology for predicting a partially observed curve from a functional data sample. The novelty of our approach relies on the selection of sample curves which form tight bands that preserve the shape of the curve to predict, making this a deep datum. The involved subsampling problem is dealt by algorithms specially designed to be used in conjunction with two different tools for computing central regions for functional data. From this merge, we obtain prediction bands for the unobserved part of the curve in question. We test our algorithms by forecasting the Spanish electricity demand and imputing missing daily temperatures. The results are consistent with our simulation that show that we can predict at the far horizon.
dc.description.sponsorship Supported by the Spanish Ministerio de Educación,Cultura y Deporte under grant FPU15/00625. Partially supported by the Spanish Ministerio de Economía y Competitividad under grant ECO2015-66593-P.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 17-12
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Depth measures
dc.subject.other Central regions
dc.subject.other Electricity demand
dc.subject.other Daily temperatures
dc.title Prediction Bands for Functional Data Based on Depth Measures
dc.type workingPaper
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. FPU15/00625
dc.relation.projectID Gobierno de España. ECO2015-66593-P
dc.type.version draft
dc.identifier.uxxi DT/0000001556
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