Prediction of extreme PM2.5 concentrations via extreme quantile regressionPrediction of extreme PM2.5 concentrations via extreme quantile regression
- Authors
- Lee, SangHyuk; Park, Seoncheol; Lim, Yaeji
- Issue Date
- May-2022
- Publisher
- 한국통계학회
- Keywords
- PM2.5 prediction; classification; quantile regression; extreme value theory
- Citation
- Communications for Statistical Applications and Methods, v.29, no.3, pp 319 - 331
- Pages
- 13
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 29
- Number
- 3
- Start Page
- 319
- End Page
- 331
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59491
- DOI
- 10.29220/CSAM.2022.29.3.319
- ISSN
- 2287-7843
2383-4757
- 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.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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