Spatial-temporal point process models are typically assessed using numerical summaries based on likelihoods or other scores which tend to have serious limitations. For instance, Models for forecasting earthquakes are currently tested prospectively, and 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, deviance residuals, and Voronoi residuals. We review some of these tests and residual methods for determining the goodness of fit of earthquake forecasting models.

(2014). Review of model evaluation techniques for space-time point processes [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/31632

Review of model evaluation techniques for space-time point processes

2014-01-01

Abstract

Spatial-temporal point process models are typically assessed using numerical summaries based on likelihoods or other scores which tend to have serious limitations. For instance, Models for forecasting earthquakes are currently tested prospectively, and 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, deviance residuals, and Voronoi residuals. We review some of these tests and residual methods for determining the goodness of fit of earthquake forecasting models.
2014
Schoenberg, F. P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/31632
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