A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression-Reference-Cited by-同舟云学术

A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression

Published:2022-06-11 Issue:52 Volume:29 Page:78265-78291
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Yang Shaomei,Wu Haoyue^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-022-21318-3.pdf

Reference42 articles.

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3. Cao MN, Tian P, Li GR (2021) Study on Lasso penalized quantile regression of optimal portfolio. J Syst Sci Math Sci 1–17

4. Geng G, Zhang Q, Martin RV et al (2015) Estimating long-term PM2.5, concentrations in China using satellite-based aerosol optical depth and a chemical transport model. Remote Sens Environ 166:262–270

5. He YY, Xu QF, Yang SL et al (2013) A power load probability density forecasting method based on RBF neural network quantile regression. Zhongguo Dianji Gongcheng Xuebao/Proc Chin Soc Electr Eng 33(1):93–98

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