dc.creatorBoente Boente, Graciela Lina
dc.creatorKudraszow, Nadia Laura
dc.date.accessioned2022-08-05T19:14:01Z
dc.date.accessioned2022-10-15T14:43:56Z
dc.date.available2022-08-05T19:14:01Z
dc.date.available2022-10-15T14:43:56Z
dc.date.created2022-08-05T19:14:01Z
dc.date.issued2021-09
dc.identifierBoente Boente, Graciela Lina; Kudraszow, Nadia Laura; Robust smoothed canonical correlation analysis for functional data; Statistica Sinica; Statistica Sinica; 32; 9-2021; 1-32
dc.identifier1017-0405
dc.identifierhttp://hdl.handle.net/11336/164435
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4398264
dc.description.abstractThis paper provides robust estimators for the first canonical correlation anddirections of random elements on Hilbert separable spaces by using robust association andscale measures combined with basis expansion and/or penalizations as a regularizationtool. Under regularity conditions, the resulting estimators are consistent. The finitesample performance of our proposal is illustrated through a simulation study that showsthat, as expected, the robust method outperforms the existing classical procedure whenthe data are contaminated. A real data example is also presented.
dc.languageeng
dc.publisherStatistica Sinica
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.5705/ss.202020.0084
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2011.10576
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCANONICAL CORRELATION ANALYSIS
dc.subjectFUNCTIONAL DATA
dc.subjectROBUSTNESS
dc.subjectSMOOTHING
dc.titleRobust smoothed canonical correlation analysis for functional data
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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