Semiparametric Bayesian forecasting of spatiotemporal earthquake occurrences

DOI10.1214/21-AOAS1554zbMath1496.62198arXiv2002.01706OpenAlexW4297333606MaRDI QIDQ2080719

Aleksandar A. Kolev, Gordon J. Ross

Publication date: 10 October 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.01706

zbMATH Keywords

ETASBayesian analysisDirichlet processseismologyspatial analysisHawkes processItalyaftershocksKDE

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Seismology (including tsunami modeling), earthquakes (86A15) Geostatistics (86A32) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Uses Software

• etasFLP
• SEDA

Cites Work

• Unnamed Item
• Bayesian inference for Hawkes processes
• Nonstationary ETAS models for nonstandard earthquakes
• On a class of Bayesian nonparametric estimates: I. Density estimates
• Maximum likelihood identification of neural point process systems
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• Space-time point-process models for earthquake occurrences
• Inference for ETAS models with non-Poissonian mainshock arrival times
• Spatially inhomogeneous background rate estimators and uncertainty quantification for nonparametric Hawkes point process models of earthquake occurrences
• A Bayesian analysis of some nonparametric problems
• Estimation of Space–Time Branching Process Models in Seismology Using an EM–Type Algorithm
• A cluster process representation of a self-exciting process
• Stochastic Declustering of Space-Time Earthquake Occurrences
• An Introduction to the Theory of Point Processes
• Self-Exciting Point Process Modeling of Crime
• Spectra of some self-exciting and mutually exciting point processes

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