Assessment of point process models for earthquake forecasting

DOI10.1214/13-STS440zbMath1331.86016arXiv1312.5934OpenAlexW1981083813MaRDI QIDQ5965039

Frederic Paik Schoenberg, Andrew Bray

Publication date: 2 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.5934

zbMATH Keywords

earthquakespoint processresidual analysismodel assessmentspatial-temporal statisticssuper-thinning

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Seismology (including tsunami modeling), earthquakes (86A15) Applications of statistics to physics (62P35) Geostatistics (86A32) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Research exposition (monographs, survey articles) pertaining to geophysics (86-02) Research exposition (monographs, survey articles) pertaining to probability theory (60-02)

Related Items (9)

Investigating Clustering and Violence Interruption in Gang-Related Violent Crime Data Using Spatial–Temporal Point Processes With Covariates ⋮ A review of self-exciting spatio-temporal point processes and their applications ⋮ Model Checking for Hidden Markov Models ⋮ Simulations of local Moran’s index in a spatio-temporal setting ⋮ Comparative evaluation of point process forecasts ⋮ Using machine learning for model benchmarking and forecasting of depletion-induced seismicity in the Groningen gas field ⋮ Generalized evolutionary point processes: model specifications and model comparison ⋮ Voronoi residual analysis of spatial point process models with applications to California earthquake forecasts ⋮ Space-time inhomogeneous background intensity estimators for semi-parametric space-time self-exciting point process models

Uses Software

• pyuvdata

Cites Work

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