Penalized B-spline estimator for regression functions using total variation penalty
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jhong, Jae-Hwan | - |
dc.contributor.author | Koo, Ja-Yong | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.date.accessioned | 2021-09-03T06:40:19Z | - |
dc.date.available | 2021-09-03T06:40:19Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-05 | - |
dc.identifier.issn | 0378-3758 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/83583 | - |
dc.description.abstract | We carry out a study on a penalized regression spline estimator with total variation penalty. In order to provide a spatially adaptive method, we consider total variation penalty for the estimating regression function. This paper adopts B-splines for both numerical implementation and asymptotic analysis because they have small supports, so the information matrices are sparse and banded. Once we express the estimator with a linear combination of B-splines, the coefficients are estimated by minimizing a penalized residual sum of squares. A new coordinate descent algorithm is introduced to handle total variation penalty determined by the B-spline coefficients. For large-sample inference, a nonasymptotic oracle inequality for penalized B-spline estimators is obtained. The oracle inequality is then used to show that the estimator is an optimal adaptive for the estimation of the regression function up to a logarithm factor. (C) 2017 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | VARIABLE SELECTION | - |
dc.subject | MODELS | - |
dc.subject | LASSO | - |
dc.subject | REGULARIZATION | - |
dc.subject | PATHS | - |
dc.title | Penalized B-spline estimator for regression functions using total variation penalty | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jhong, Jae-Hwan | - |
dc.contributor.affiliatedAuthor | Koo, Ja-Yong | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.1016/j.jspi.2016.12.003 | - |
dc.identifier.scopusid | 2-s2.0-85008598998 | - |
dc.identifier.wosid | 000394199600006 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.184, pp.77 - 93 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.title | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.volume | 184 | - |
dc.citation.startPage | 77 | - |
dc.citation.endPage | 93 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | LASSO | - |
dc.subject.keywordPlus | REGULARIZATION | - |
dc.subject.keywordPlus | PATHS | - |
dc.subject.keywordAuthor | Adaptive estimation | - |
dc.subject.keywordAuthor | Coordinate descent algorithm | - |
dc.subject.keywordAuthor | LASSO | - |
dc.subject.keywordAuthor | Oracle inequalities | - |
dc.subject.keywordAuthor | Penalized least squares | - |
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