Earthquake forecast enrichment scores

Using earthquake intensities to forecast earthquake occurrence times

It is well known that earthquakes do not occur randomly in space and time. Foreshocks, aftershocks, precursory activation, and quiescence are just some of the patterns recognized by seismologists. Using the Pattern Informatics technique along with relative intensity analysis, we create a scoring method based on time dependent relative operating characteristic diagrams and show that the occurren...

Forecast and analysis assessment through skill scores

Paleoseismic interevent times interpreted for an unsegmented earthquake rupture forecast

[1] Forecasters want to consider an increasingly rich variety of earthquake ruptures. Past occurrence is captured in part by paloeseismic observations, which necessarily see threedimensional ruptures only at a point. This has not been a problem before, because forecasts have assumed that faults are segmented, and that repeated ruptures occur uniformly along them. A technique is now required to ...

Weighted likelihood ratio scores for evaluating forecast densities in tails

We propose and evaluate several new scoring rules based on likelihood ratios, for comparing forecast densities in the context of VaR modelling and expected loss estimation. Our approach is motivated by the observation that existing scoring rules tend to favour fat-tailed models when compared with thin-tailed models. Rather than restricting the weight functions, we impose some restrictions on th...

Gene Set Enrichment Analysis Using Non-parametric Scores

Gene Set Enrichment Analysis (GSEA) is a well-known technique used for studying groups of functionally related genes and their correlation with phenotype. This method creates a ranked list of genes, which is used to calculate an enrichment score. In this work, we introduce two different metrics for gene ranking in GSEA, namely the Wilcoxon and the Baumgartner-Weiß-Schindler tests. The advantage...