Why calibrating LR-systems is best practice. A reaction to "The evaluation of evidence for microspectrophotometry data using functional data analysis", in FSI 305.
Calibration
Feature-based
Forensic
Functional data analysis
Likelihood ratio
Microspectrophotometry
Score-based
Validation
Journal
Forensic science international
ISSN: 1872-6283
Titre abrégé: Forensic Sci Int
Pays: Ireland
ID NLM: 7902034
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
02
04
2020
revised:
08
06
2020
accepted:
20
06
2020
pubmed:
15
7
2020
medline:
15
7
2020
entrez:
15
7
2020
Statut:
ppublish
Résumé
In their paper "The evaluation of evidence for microspectrophotometry data using functional data analysis", in FSI 305, Aitken et al. present a likelihood-ratio (LR) system for their data. We show the values generated by this system cannot be interpreted as LRs: they are ill-calibrated and should be interpreted as discriminating scores. We demonstrate how to transform the scores to well-calibrated LRs using a post-hoc calibrating step. Also, we address criticisms of calibration posited by Aitken et al. We conclude by noting that ill-calibrated LR-values are misleadingly small or large. Therefore calibration should be measured and, if necessary, corrected for. The corrected LR-values (instead of the discriminating scores) can be used to update the prior odds in Bayes rule.
Identifiants
pubmed: 32663721
pii: S0379-0738(20)30250-4
doi: 10.1016/j.forsciint.2020.110388
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
110388Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.