Assessment of Point Process Models for Earthquake Forecasting

Abstract

Models for forecasting earthquakes are currently tested prospectively in well-organized testing centers, using data collected after the models and their parameters are completely specified. The extent to which these models agree with the data is typically assessed using a variety of numerical tests, which unfortunately have low power and may be misleading for model comparison purposes. Promising alternatives exist, especially residual methods such as super-thinning and Voronoi residuals. This article reviews some of these tests and residual methods for determining the goodness of fit of earthquake forecasting models.

Published in at http://dx.doi.org/10.1214/13-STS440 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• Apparent Clustering and Apparent Background Earthquakes Biased by Undetected Seismicity
• Residual analysis methods for space--time point processes with applications to earthquake forecast models in California

Cited by in corpus (7)

• A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications
• Voronoi residual analysis of spatial point process models with applications to California earthquake forecasts
• Comparative evaluation of point process forecasts
• Enhancing the statistical evaluation of earthquake forecasts -- An application to Italy
• Generalized Evolutionary Point Processes: Model Specifications and Model Comparison
• Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences
• Goodness-of-Fit Test for Mismatched Self-Exciting Processes