Information-Theoretical Assessment of the Performance of Likelihood Ratio Computation Methods

Daniel Ramos*, Joaquin Gonzalez-Rodriguez, Grzegorz Zadora, Colin Aitken

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Performance of likelihood ratio (LR) methods for evidence evaluation has been represented in the past using, for example, Tippett plots. We propose empirical cross-entropy (ECE) plots as a metric of accuracy based on the statistical theory of proper scoring rules, interpretable as information given by the evidence according to information theory, which quantify calibration of LR values. We present results with a case example using a glass database from real casework, comparing performance with both Tippett and ECE plots. We conclude that ECE plots allow clearer comparisons of LR methods than previous metrics, allowing a theoretical criterion to determine whether a given method should be used for evidence evaluation or not, which is an improvement over Tippett plots. A set of recommendations for the use of the proposed methodology by practitioners is also given.

Original languageEnglish
Pages (from-to)1503-1518
Number of pages16
JournalJournal of Forensic Sciences
Volume58
Issue number6
Early online date23 Jul 2013
DOIs
Publication statusPublished - Nov 2013

Keywords

  • forensic science
  • evidence evaluation
  • likelihood ratio
  • empirical cross-entropy
  • performance assessment
  • glass evidence
  • information theory
  • MATHEMATICAL-THEORY
  • FORENSIC-SCIENCE
  • MODEL
  • CLASSIFICATION
  • COMMUNICATION
  • CASEWORK

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