Transformations for compositional data with zeros with an application to forensic evidence evaluation

Neocleous, T. , Aitken, C. and Zadora, G. (2011) Transformations for compositional data with zeros with an application to forensic evidence evaluation. Chemometrics and Intelligent Laboratory Systems, 109(1), pp. 77-85. (doi: 10.1016/j.chemolab.2011.08.003)

Abstract

In forensic science likelihood ratios provide a natural way of computing the value of evidence under competing propositions such as "the compared samples have originated from the same object" (prosecution) and "the compared samples have originated from different objects" (defence). We use a two-level multivariate likelihood ratio model for comparison of forensic glass evidence in the form of elemental composition data under three data transformations: the logratio transformation, a complementary log-log type transformation and a hyperspherical transformation. The performances of the three transformations in the evaluation of evidence are assessed in simulation experiments through use of the proportions of false negatives and false positives.

• Item Type: Articles
• Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems 109:1 (2011), DOI: 10.1016/j.chemolab.2011.08.003
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Neocleous, Dr Tereza
• Authors: Neocleous, T., Aitken, C., and Zadora, G.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Chemometrics and Intelligent Laboratory Systems
• Publisher: Elsevier
• ISSN: 0169-7439
• ISSN (Online): 1873-3239
• Published Online: 16 August 2011
• Copyright Holders: Copyright © 2011 Elsevier
• First Published: First published in Chemometrics and Intelligent Laboratory Systems 109(1):77-85
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher