Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

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dc.creatorLian, Heng
dc.date2007-02-05
dc.date2007-02-05
dc.date.accessioned2026-07-07T12:13:24Z
dc.date.available2026-07-07T12:13:24Z
dc.descriptionAn extension of reproducing kernel Hilbert space (RKHS) theory provides a new framework for modeling functional regression models with functional responses. The approach only presumes a general nonlinear regression structure as opposed to previously studied linear regression models. Generalized cross-validation (GCV) is proposed for automatic smoothing parameter estimation. The new RKHS estimate is applied to both simulated and real data as illustrations.
dc.identifierhttps://arxiv.org/abs/math/0702120
dc.identifierhttp://arxiv.org/abs/math/0702120
dc.identifierCanadian Journal of Statistics, 35(4):597-606, 2007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/210836
dc.subjectStatistics Theory
dc.titleNonlinear functional models for functional responses in reproducing kernel Hilbert spaces
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