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The evaluation of evidence for auto‐correlated data in relation to traces of cocaine on banknotes
Author(s) -
Wilson Amy,
Aitken Colin,
Sleeman Richard,
Carter James
Publication year - 2015
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12073
Subject(s) - markov chain , econometrics , independence (probability theory) , psychology , relation (database) , computer science , statistics , mathematics , machine learning , data mining
Summary 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.