A grouped spatial-temporal model for PM<sub>2.5</sub> data and its applications on outlier detection-Reference-Cited by-同舟云学术

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A grouped spatial-temporal model for PM_2.5 data and its applications on outlier detection

Published:2022-06-07 Issue: Volume: Page:1-13
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Guo Baishan¹,Wang Lei²,Pan Rui³^ORCID,Zhu Xuening⁴^ORCID

Affiliation:

1. Meta Platforms, Inc, Menlo Park, California, USA

2. Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

3. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China

4. School of Data Science, Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

Shanghai Sailing Program for Youth Science and Technology Excellence

Publisher

Informa UK Limited

Subject

Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2022.2081707

Reference46 articles.

1. Effect of spatial outliers on the regression modelling of air pollutant concentrations: A case study in Japan

2. A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

3. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project

4. Control chart and Six sigma based algorithms for identification of outliers in experimental data, with an application to particulate matter PM 10

5. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information

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