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: Liu, M
Yu, Z
Yu, K
Kong, F
Tian, M
Keywords: Bayesian quantile regression;dynamic models;latent Gaussian process;Markov chain Monte Carlo;PM₂.₅ concentration
Issue Date: 29-Jul-2025
Publisher: Oxford University Press on behalf of The Royal Statistical Society
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, 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 concentration data for the northeastern United States, as referenced in Appendix C, can be obtained from the report by Paciorek et al. (2009).
URI: https://bura.brunel.ac.uk/handle/2438/31681
DOI: https://doi.org/10.1093/jrsssc/qlaf040
ISSN: 0035-9254
Other Identifiers: ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402
ORCiD: Maozai Tian https://orcid.org/0009-0001-9180-5554
Article number: qlaf040
Appears in Collections:Dept of Mathematics Embargoed Research Papers

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FullText.pdfEmbargoed until 29 July 2026. Copyright © The Royal Statistical Society 2025.. Published by Oxford University Press. This is a pre-copy-editing, author-produced version of an article accepted for publication in Journal of the Royal Statistical Society Series C: Applied Statistics following peer review. The definitive publisher-authenticated version, Miaorou Liu, Zhen Yu, Keming Yu, Fansheng Kong, Maozai Tian, Spatiotemporal dynamic quantile regression models with applications to particulate matter concentration data, Journal of the Royal Statistical Society Series C: Applied Statistics, 2025;, qlaf040, is available online at: https://doi.org/10.1093/jrsssc/qlaf040 (see: https://global.oup.com/academic/rights/permissions/autperm/?cc=gb&lang=en&).2.08 MBAdobe PDFView/Open


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