Structured kernel quantile regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koo, Ja-Yong | - |
dc.contributor.author | Park, Kwi Wook | - |
dc.contributor.author | Kim, Byung Won | - |
dc.contributor.author | Kim, Kwang-Rae | - |
dc.contributor.author | Park, Changyi | - |
dc.date.accessioned | 2021-09-06T05:29:49Z | - |
dc.date.available | 2021-09-06T05:29:49Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-01-01 | - |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/104239 | - |
dc.description.abstract | Quantile regression can provide more useful information on the conditional distribution of a response variable given covariates while classical regression provides informations on the conditional mean alone. In this paper, we propose a structured quantile estimation methodology in a nonparametric function estimation setup. Through the functional analysis of variance decomposition, the optimization of the proposed method can be solved using a series of quadratic and linear programmings. Our method automatically selects relevant covariates by adopting a lasso-type penalty. The performance of the proposed methodology is illustrated through numerical examples on both simulated and real data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.subject | SUPPORT VECTOR MACHINES | - |
dc.subject | COMPONENT SELECTION | - |
dc.title | Structured kernel quantile regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Koo, Ja-Yong | - |
dc.identifier.doi | 10.1080/00949655.2011.631923 | - |
dc.identifier.scopusid | 2-s2.0-84946029967 | - |
dc.identifier.wosid | 000313033800013 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.83, no.1, pp.179 - 190 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.title | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.volume | 83 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 179 | - |
dc.citation.endPage | 190 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | COMPONENT SELECTION | - |
dc.subject.keywordAuthor | functional ANOVA decomposition | - |
dc.subject.keywordAuthor | lasso | - |
dc.subject.keywordAuthor | linear program | - |
dc.subject.keywordAuthor | quadratic program | - |
dc.subject.keywordAuthor | structured kernel | - |
dc.subject.keywordAuthor | 62G08 | - |
dc.subject.keywordAuthor | 62F07 | - |
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