An introduction to (smoothing spline) ANOVA models in RKHS with examples in geographical data, medicine, atmospheric science and machine learning

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dc.creatorWahba, Grace
dc.date2004-10-19
dc.date.accessioned2026-07-07T08:06:33Z
dc.date.available2026-07-07T08:06:33Z
dc.descriptionSmoothing Spline ANOVA (SS-ANOVA) models in reproducing kernel Hilbert spaces (RKHS) provide a very general framework for data analysis, modeling and learning in a variety of fields. Discrete, noisy scattered, direct and indirect observations can be accommodated with multiple inputs and multiple possibly correlated outputs and a variety of meaningful structures. The purpose of this paper is to give a brief overview of the approach and describe and contrast a series of applications, while noting some recent results.
dc.descriptionThis note has appeared in the Proceedings of the 13th IFAC Symposium on System Identification 2003, Rotterdam, 549-559
dc.identifierhttps://arxiv.org/abs/math/0410419
dc.identifierhttp://arxiv.org/abs/math/0410419
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130647
dc.subjectStatistics Theory
dc.subject62G08, 62-02, 62-07
dc.titleAn introduction to (smoothing spline) ANOVA models in RKHS with examples in geographical data, medicine, atmospheric science and machine learning
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