Prediction of extreme PM2.5 concentrations via extreme quantile regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, SangHyuk | - |
dc.contributor.author | Park, Seoncheol | - |
dc.contributor.author | Lim, Yaeji | - |
dc.date.accessioned | 2022-12-02T11:40:17Z | - |
dc.date.available | 2022-12-02T11:40:17Z | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.issn | 2383-4757 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59491 | - |
dc.description.abstract | In this paper, we develop a new statistical model to forecast the PM_{2.5} level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts `very bad' cases of the PM_{2.5} level well. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국통계학회 | - |
dc.title | Prediction of extreme PM2.5 concentrations via extreme quantile regression | - |
dc.title.alternative | Prediction of extreme PM2.5 concentrations via extreme quantile regression | - |
dc.type | Article | - |
dc.identifier.doi | 10.29220/CSAM.2022.29.3.319 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.29, no.3, pp 319 - 331 | - |
dc.identifier.kciid | ART002846034 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000811166000002 | - |
dc.identifier.scopusid | 2-s2.0-85132325623 | - |
dc.citation.endPage | 331 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 319 | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 29 | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | PM2.5 prediction | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | quantile regression | - |
dc.subject.keywordAuthor | extreme value theory | - |
dc.subject.keywordPlus | PARAMETERS | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | BURDEN | - |
dc.subject.keywordPlus | MODEL | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
dc.description.journalRegisteredClass | kciCandi | - |
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