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Article: Analysis of regularized least squares for functional linear regression model

TitleAnalysis of regularized least squares for functional linear regression model
Authors
Tong, HongzhiNg, Michael
KeywordsRegularized least squares
Reproducing kernel Hilbert space
Functional linear regression
Learning rate
Issue Date2018
Citation
Journal of Complexity, 2018, v. 49, p. 85-94 How to Cite?
DOI: http://dx.doi.org/10.1016/j.jco.2018.08.001
Abstract© 2018 In this paper, we study and analyze the regularized least squares for functional linear regression model. The approach is to use the reproducing kernel Hilbert space framework and the integral operators. We show with a more general and realistic assumption on the reproducing kernel and input data statistics that the rate of excess prediction risk by the regularized least squares is minimax optimal.
Persistent Identifierhttp://hdl.handle.net/10722/276777
ISSN
0885-064X
2023 Impact Factor: 1.8
2023 SCImago Journal Rankings: 1.115
ISI Accession Number ID
WOS:000446152400006

 

DC FieldValueLanguage
dc.contributor.authorTong, Hongzhi-
dc.contributor.authorNg, Michael-
dc.date.accessioned2019-09-18T08:34:37Z-
dc.date.available2019-09-18T08:34:37Z-
dc.date.issued2018-
dc.identifier.citationJournal of Complexity, 2018, v. 49, p. 85-94-
dc.identifier.issn0885-064X-
dc.identifier.urihttp://hdl.handle.net/10722/276777-
dc.description.abstract© 2018 In this paper, we study and analyze the regularized least squares for functional linear regression model. The approach is to use the reproducing kernel Hilbert space framework and the integral operators. We show with a more general and realistic assumption on the reproducing kernel and input data statistics that the rate of excess prediction risk by the regularized least squares is minimax optimal.-
dc.languageeng-
dc.relation.ispartofJournal of Complexity-
dc.subjectRegularized least squares-
dc.subjectReproducing kernel Hilbert space-
dc.subjectFunctional linear regression-
dc.subjectLearning rate-
dc.titleAnalysis of regularized least squares for functional linear regression model-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jco.2018.08.001-
dc.identifier.scopuseid_2-s2.0-85051823332-
dc.identifier.volume49-
dc.identifier.spage85-
dc.identifier.epage94-
dc.identifier.eissn1090-2708-
dc.identifier.isiWOS:000446152400006-
dc.identifier.issnl0885-064X-

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