Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31681
Title: Spatiotemporal dynamic quantile regression models with applications to particulate matter concentration data
Authors: Yu, K
Liu, M
Yu, Z
Kong, F
Tian, M
Keywords: Bayesian quantile regression;dynamic models;latent Gaussian process;Markov chain Monte Carlo;PM₂.₅concentration
Issue Date: 29-Jul-2025
Publisher: Oxford University Press
Citation: Liu, M. et al. (2025). ‘Spatiotemporal dynamic quantile regression models with applications to particulate matter concentration data’, Journal of the Royal Statistical Society Series C: Applied Statistics. Vol. 0 (ahead of print), pp. 1 - 44. doi:10.1093/jrsssc/qlaf040.
Abstract: The spatiotemporal evolution of high-concentration PM₂.₅ is a significant concern, especially when working with datasets that display complex spatiotemporal dependencies. Identifying the spatiotemporal covariance function in such cases is often challenging. The paper proposes a novel spatiotemporal dynamic quantile regression model (STDQM) that captures the temporal evolution of spatial processes by integrating a latent Gaussian process. To accommodate more complex data structures and expand the inferential capabilities of the asymmetric Laplace distribution within the standard quantile regression framework, the paper also proposes a more flexible STDQM based on the generalized asymmetric Laplace distribution. A substantial number of simulation studies demonstrate that the proposed methods significantly enhance inference quality and prediction accuracy compared to existing alternative methods. The new method is employed to analyse the influencing factors and spatiotemporal evolution of PM₂.₅ concentrations at various quantile levels in Lombardy, Italy, from 2016 to 2021.
Description: ● Data availability - The Agrimonia dataset used in the empirical application can be accessed from the Zenodo website at https://zenodo.org/. The PM₂.₅ concentration data for the northeastern United States, as referenced in Appendix C [𝘩𝘵𝘵𝘱𝘴://𝘢𝘤𝘢𝘥𝘦𝘮𝘪𝘤.𝘰𝘶𝘱.𝘤𝘰𝘮/𝘫𝘳𝘴𝘴𝘴𝘤/𝘢𝘥𝘷𝘢𝘯𝘤𝘦-𝘢𝘳𝘵𝘪𝘤𝘭𝘦/𝘥𝘰𝘪/10.1093/𝘫𝘳𝘴𝘴𝘴𝘤/𝘲𝘭𝘢𝘧040/8217388?𝘴𝘦𝘢𝘳𝘤𝘩𝘳𝘦𝘴𝘶𝘭𝘵=1#𝘢𝘱𝘱3], can be obtained from the report by Paciorek et al. (2009). [𝘩𝘵𝘵𝘱𝘴://𝘥𝘰𝘪.𝘰𝘳𝘨/10.1214/08-𝘈𝘖𝘈𝘚204]
● Set phrase / statement can be found on the last page
URI: http://bura.brunel.ac.uk/handle/2438/31681
DOI: https://doi.org/10.1093/jrsssc/qlaf040
ISSN: 0035-9254
Other Identifiers: Article No.: qlaf040
ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402
ORCiD: Mao-zai Tian https://orcid.org/0009-0001-9180-5554
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