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dc.contributor.authorElías Fernández, Antonio
dc.contributor.authorJiménez, Raúl
dc.contributor.authorLin Shang, Han
dc.date.accessioned2023-01-25T07:49:09Z
dc.date.available2023-01-25T07:49:09Z
dc.date.issued2022-09-26
dc.identifier.citationElías, A., Jiménez, R. & Shang, H.L. Depth-based reconstruction method for incomplete functional data. Comput Stat (2022). https://doi.org/Elías, A., Jiménez, R. & Shang, H.L. Depth-based reconstruction method for incomplete functional data. Comput Stat (2022). https://doi.org/10.1007/s00180-022-01282-9es_ES
dc.identifier.urihttps://hdl.handle.net/10630/25781
dc.description.abstractThe problem of estimating missing fragments of curves from a functional sample has been widely considered in the literature. However, most reconstruction methods rely on estimating the covariance matrix or the components of its eigendecomposition, which may be difficult. In particular, the estimation accuracy might be affected by the complexity of the covariance function, the noise of the discrete observations, and the poor availability of complete discrete functional data. We introduce a non-parametric alternative based on depth measures for partially observed functional data. Our simulations point out that the benchmark methods perform better when the data come from one population, curves are smooth, and there is a large proportion of complete data. However, our approach is superior when considering more complex covariance structures, non-smooth curves, and when the proportion of complete functions is scarce. Moreover, even in the most severe case of having all the functions incomplete, our method provides good estimates; meanwhile, the competitors are unable. The methodology is illustrated with two real data sets: the Spanish daily temperatures observed in different weather stations and the age-specific mortality by prefectures in Japan. They highlight the interpretability potential of the depth-based method.es_ES
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidad de Málaga / CBUA. Not applicable.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectReconstrucciónes_ES
dc.subject.otherFunctional dataes_ES
dc.subject.otherPartially observed dataes_ES
dc.subject.otherReconstructiones_ES
dc.subject.otherDepth measureses_ES
dc.titleDepth-based reconstruction method for incomplete functional dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1007/s00180-022-01282-9
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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