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Bayesian Modeling of Skewed Spatio-Temporal Data Using a Flexible Random Field with Matérn Correlation and Adaptive Hamiltonian Algorithm
Omid Karimi * , Fatemeh Hosseini
Abstract:   (21 Views)
Spatio-temporal data often exhibit skewed distributions, which pose challenges for accurate modeling. Skew Gaussian random fields are among the common approaches for analyzing such data, although some existing models suffer from computational complexity and identifiability issues. In this paper, a Bayesian framework is proposed for modeling skewed spatio-temporal data based on a flexible closed skew Gaussian random field, which possesses desirable properties such as identifiability and closure under marginalization and conditioning. By employing the Matérn correlation function, the proposed model provides adequate flexibility for capturing spatio-temporal dependence structures. Bayesian inference is performed using the Hamiltonian Monte Carlo algorithm, and a simulation study is conducted to compare its performance with conventional Markov Chain Monte Carlo methods. Finally, the performance of the proposed model was also evaluated using observed PM-10 air pollution data.
Keywords: Adaptive Hamiltonian Monte Carlo, Matérn Correlation Function, Low-Rank Representation, Bayesian Hierarchical Model, PM10 Air Pollution.
Full-Text [PDF 2731 kb]   (13 Downloads)    
Type of Study: Research | Subject: Spatial Statistics
Received: 2026/05/25 | Accepted: 2027/03/1
References
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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