Literature DB >> 19608360

Different likelihood ratio approaches to evaluate the strength of evidence of MDMA tablet comparisons.

Annabel Bolck1, Céline Weyermann, Laurence Dujourdy, Pierre Esseiva, Jorrit van den Berg.   

Abstract

Two likelihood ratio (LR) approaches are presented to evaluate the strength of evidence of MDMA tablet comparisons. The first one is based on a more 'traditional' comparison of MDMA tablets by using distance measures (e.g., Pearson correlation distance or a Euclidean distance). In this approach, LRs are calculated using the distribution of distances between tablets of the same-batch and that of different-batches. The second approach is based on methods used in some other fields of forensic comparison. Here LRs are calculated based on the distribution of values of MDMA tablet characteristics within a specific batch and from all batches. The data used in this paper must be seen as examples to illustrate both methods. In future research the methods can be applied to other and more complex data. In this paper, the methods and their results are discussed, considering their performance in evidence evaluation and several practical aspects. With respect to evidence in favor of the correct hypothesis, the second method proved to be better than the first one. It is shown that the LRs in same-batch comparisons are generally higher compared to the first method and the LRs in different-batch comparisons are generally lower. On the other hand, for operational purposes (where quick information is needed), the first method may be preferred, because it is less time consuming. With this method a model has to be estimated only once in a while, which means that only a few measurements have to be done, while with the second method more measurements are needed because each time a new model has to be estimated.

Entities:  

Year:  2009        PMID: 19608360     DOI: 10.1016/j.forsciint.2009.06.006

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  2 in total

1.  Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data.

Authors:  Javier Franco-Pedroso; Daniel Ramos; Joaquin Gonzalez-Rodriguez
Journal:  PLoS One       Date:  2016-02-22       Impact factor: 3.240

2.  Source-anchored, trace-anchored, and general match score-based likelihood ratios for camera device identification.

Authors:  Stephanie Reinders; Yong Guan; Danica Ommen; Jennifer Newman
Journal:  J Forensic Sci       Date:  2022-02-06       Impact factor: 1.717

  2 in total

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