Hierarchical Bayesian modeling of spatio-temporal area-interaction processes

DOI10.1016/j.csda.2021.107349OpenAlexW3200359109WikidataQ108863847 ScholiaQ108863847MaRDI QIDQ2072383

Jiaxun Chen, Athanasios C. Micheas, Scott H. Holan

Publication date: 26 January 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2021.107349

zbMATH Keywords

Bayesian analysishierarchical modelautoregressive priordouble Metropolis-Hastings within Gibbs samplerspatio-temporal area-interaction process

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Markov chain Monte Carlo: can we trust the third significant figure?
• Area-interaction point processes
• Spatiotemporal Prediction for Log-Gaussian Cox Processes
• Modelling Aggregation on the Large Scale and Regularity on the Small Scale in Spatial Point Pattern Datasets
• A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants
• Handbook of Spatial Statistics
• A model for clustering
• A note on Strauss's model for clustering
• Markov Point Processes and Their Applications
• Bayesian Inference in the Presence of Intractable Normalizing Functions
• An Introduction to the Theory of Point Processes
• Statistical Analysis and Modelling of Spatial Point Patterns
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
• Spectra of some self-exciting and mutually exciting point processes
• Bayesian Inference for Matérn Repulsive Processes
• Cox Point Processes: Why One Realisation Is Not Enough
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