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Testing for calibration discrepancy of reported likelihood ratios in forensic science
The Journal of the Royal Statistical Society, Series A (Statistics in Society) ( IF 2 ) Pub Date : 2021-09-15 , DOI: 10.1111/rssa.12747
Jan Hannig 1, 2 , Hari Iyer 2
Affiliation  

The use of likelihood ratios for quantifying the strength of forensic evidence in criminal cases is gaining widespread acceptance in many forensic disciplines. Although some forensic scientists feel that subjective likelihood ratios are a reasonable way of expressing expert opinion regarding strength of evidence in criminal trials, legal requirements of reliability of expert evidence in the United Kingdom, United States and some other countries have encouraged researchers to develop likelihood ratio systems based on statistical modelling using relevant empirical data. Many such systems exhibit exceptional power to discriminate between the scenario presented by the prosecution and an alternate scenario implying the innocence of the defendant. However, such systems are not necessarily well calibrated. Consequently, verbal explanations to triers of fact, by forensic experts, of the meaning of the offered likelihood ratio may be misleading. In this article, we put forth a statistical approach for testing the calibration discrepancy of likelihood ratio systems using ground truth known empirical data. We provide point estimates as well as confidence intervals for the calibration discrepancy. Several examples, previously discussed in the literature, are used to illustrate our method. Results from a limited simulation study concerning the performance of the proposed approach are also provided.

中文翻译:

测试法医学中报告的似然比的校准差异

使用似然比来量化刑事案件中法医证据的强度正在许多法医学科中获得广泛接受。尽管一些法医科学家认为主观似然比是在刑事审判中就证据强度表达专家意见的一种合理方式,但英国、美国和其他一些国家对专家证据可靠性的法律要求鼓励研究人员开发似然比基于使用相关经验数据的统计建模的系统。许多此类系统表现出非凡的能力,可以区分控方提出的情景和暗示被告无罪的替代情景。然而,这样的系统不一定是经过良好校准的。所以,法医专家对所提供似然比含义的口头解释可能具有误导性。在本文中,我们提出了一种统计方法,用于使用已知的真实经验数据来测试似然比系统的校准差异。我们提供点估计以及校准差异的置信区间。之前在文献中讨论过的几个例子用于说明我们的方法。还提供了有关所提出方法的性能的有限模拟研究的结果。我们提供点估计以及校准差异的置信区间。之前在文献中讨论过的几个例子用于说明我们的方法。还提供了关于所提出方法的性能的有限模拟研究的结果。我们提供点估计以及校准差异的置信区间。之前在文献中讨论过的几个例子用于说明我们的方法。还提供了有关所提出方法的性能的有限模拟研究的结果。
更新日期:2021-09-15
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