Measuring calibration of likelihood-ratio systems: A comparison of four metrics, including a new metric devPAV.

Numerical likelihood-ratio (LR) systems aim to calculate evidential strength for forensic evidence evaluation. Calibration of such LR-systems is essential: one does not want to over- or understate the strength of the evidence. Metrics that measure calibration differ in sensitivity to errors in calibration of such systems. In this paper we compare four calibration metrics by a simulation study based on Gaussian Log LR-distributions. Three calibration metrics are taken from the literature (Good, 1985; Royall, 1997; Ramos and Gonzalez-Rodriguez, 2013) [1-3], and a fourth metric is proposed by us. We evaluated these metrics by two performance criteria: differentiation (between well- and ill-calibrated LR-systems) and stability (of the value of the metric for a variety of well-calibrated LR-systems). Two metrics from the literature (the expected values of LR and of 1/LR, and the rate of misleading evidence stronger than 2) do not behave as desired in many simulated conditions. The third one (C

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