Forecasting high levels of PM10 in Korea based on the principal expectile component regression†
Dongkyung Lim1 · Yaeji Lim2
12 Department of Statistics, Chung-Ang University
Correspondence to: † This research was supported by the Chung-Ang University Graduate Research Scholarship in 2021, and the National Research Foundation of Korea (NRF) funded by the Korea government (NRF-2022R1F1A1074134).
1 Postgraduate student, Department of Statistics, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: ssodong93@naver.com
2 Associate professor, Department of Statistics, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: yaeji.lim@gmail.com
1 Postgraduate student, Department of Statistics, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: ssodong93@naver.com
2 Associate professor, Department of Statistics, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: yaeji.lim@gmail.com
Received November 18, 2022; Revised December 8, 2022; Accepted December 12, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- As the level of fine dust has risen sharply recently, many studies has been conducted to analyze the data. Since exposure to fine dust is related to the occurrence of cardiovascular diseases and respiratory, it can make the mortality rate increase. Therefore, it is important to predict the extreme level of fine dust. In this paper, we consider a regression model based on the principal expectile analysis. Compare to the conventional principal component analysis, principal expectile analysis can capture variations around the tail of the data. By so doing, we predict ‘Bad’ cases of the PM10 level of 25 districts in Seoul, South Korea and compare the results with the classical principal component regression. From the results, we observe that the proposed model predicts the extreme level of fine dust better than the existing model.
- Keywords : Fine particulate matter, Principal component regression, Principal expectile component regression, PM10 prediction.
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