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Evaluation of trace evidence in the form of multivariate data

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  • C. G. G. Aitken
  • D. Lucy

Abstract

Summary. The evaluation of measurements on characteristics of trace evidence found at a crime scene and on a suspect is an important part of forensic science. Five methods of assessment for the value of the evidence for multivariate data are described. Two are based on significance tests and three on the evaluation of likelihood ratios. The likelihood ratio which compares the probability of the measurements on the evidence assuming a common source for the crime scene and suspect evidence with the probability of the measurements on the evidence assuming different sources for the crime scene and suspect evidence is a well‐documented measure of the value of the evidence. One of the likelihood ratio approaches transforms the data to a univariate projection based on the first principal component. The other two versions of the likelihood ratio for multivariate data account for correlation among the variables and for two levels of variation: that between sources and that within sources. One version assumes that between‐source variability is modelled by a multivariate normal distribution; the other version models the variability with a multivariate kernel density estimate. Results are compared from the analysis of measurements on the elemental composition of glass.

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  • C. G. G. Aitken & D. Lucy, 2004. "Evaluation of trace evidence in the form of multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 109-122, January.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:1:p:109-122
    DOI: 10.1046/j.0035-9254.2003.05271.x
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    Cited by:

    1. Ivo Alberink & Annabel Bolck & Sonja Menges, 2013. "Posterior likelihood ratios for evaluation of forensic trace evidence given a two-level model on the data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2579-2600, December.
    2. Nagler, Thomas & Czado, Claudia, 2016. "Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 69-89.
    3. Dan J. Spitzner, 2023. "Calibrated Bayes factors under flexible priors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 733-767, September.
    4. Aitken, C.G.G. & Lucy, D. & Zadora, G. & Curran, J.M., 2006. "Evaluation of transfer evidence for three-level multivariate data with the use of graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2571-2588, June.

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