Testing for calibration discrepancy of reported likelihood ratios in forensic science

Jan Hannig and Hari Iyer

Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue 1, 267-301

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssa.12747

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:185:y:2022:i:1:p:267-301

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().