Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Prediction of extreme PM2.5 concentrations via extreme quantile regression

  • Lee, SangHyuk (Department of Statistics, Chung-Ang University) ;
  • Park, Seoncheol (Department of Information Statistics, Chungbuk National University) ;
  • Lim, Yaeji (Department of Statistics, Chung-Ang University)
  • Received : 2021.10.07
  • Accepted : 2021.11.22
  • Published : 2022.05.31
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Abstract

In this paper, we develop a new statistical model to forecast the PM2.5 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 PM2.5 level well.

Keywords

Acknowledgement

This research was supported by the Chung-Ang University Graduate Research Scholarship in 2020 and the National Research Foundation of Korea (NRF) funded by the Korean government (NRF-2021R1A2B5B01001790, NRF-2021R1F1A1064096).

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