Extremal Dependence Indices: Improved Verification Measures for Deterministic Forecasts of Rare Binary Events

Abstract

erifying forecasts of rare events is challenging, in part because traditional performance measures degenerate to trivial values as events become rarer. The extreme dependency score was proposed recently as a nondegenerating measure for the quality of deterministic forecasts of rare binary events. This measure has some undesirable properties, including being both easy to hedge and dependent on the base rate. A symmetric extreme dependency score was also proposed recently, but this too is dependent on the base rate. These two scores and their properties are reviewed and the meanings of several properties, such as base-rate dependence and complement symmetry that have caused confusion are clarified. Two modified versions of the extreme dependency score, the extremal dependence index, and the symmetric extremal dependence index, are then proposed and are shown to overcome all of its shortcomings. The new measures are nondegenerating, base-rate independent, asymptotically equitable, harder to hedge, and have regular isopleths that correspond to symmetric and asymmetric relative operating characteristic curves.