The evaluation of evidence for autocorrelated data with an example relating to traces of cocaine on banknotes

Amy Wilson, Colin Aitken, Richard Sleeman, James Carter

Research output: Contribution to journalArticlepeer-review

Abstract

Much research in recent years for evidence evaluation in forensic science has focused on methods for determining the likelihood ratio in various scenarios. When the issue in question is whether evidence is associated with a person who is or is not associated with criminal activity then the problem is one of discrimination. A procedure for the determination of the likelihood ratio is developed when the evidential data are believed to be driven by an underlying latent Markov chain. Three other models that assume auto-correlated data without the underlying Markov chain are also described. The performances of these four models and a model assuming independence are compared by using data concerning traces of cocaine on banknotes.
Original languageEnglish
Pages (from-to)275-298
JournalJournal of the Royal Statistical Society: Series C
Volume64
Issue number2
Early online date23 Sep 2014
DOIs
Publication statusPublished - Feb 2015

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