Skip to main content

A Two-Level Model for Evidence Evaluation

Buy Article:

$51.00 plus tax (Refund Policy)


A random effects model using two levels of hierarchical nesting has been applied to the calculation of a likelihood ratio as a solution to the problem of comparison between two sets of replicated multivariate continuous observations where it is unknown whether the sets of measurements shared a common origin. Replicate measurements from a population of such measurements allow the calculation of both within-group and between-group variances/covariances. The within-group distribution has been modelled assuming a Normal distribution, and the between-group distribution has been modelled using a kernel density estimation procedure. A graphical method of estimating the dependency structure among the variables has been used to reduce this highly multivariate problem to several problems of lower dimension. The approach was tested using a database comprising measurements of eight major elements from each of four fragments from each of 200 glass objects and found to perform well compared with previous approaches, achieving a 15.2% false-positive rate, and a 5.5% false-negative rate. The modelling was then applied to two examples of casework in which glass found at the scene of the criminal activity has been compared with that found in association with a suspect.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: compositional data; evidence evaluation; forensic science; glass; graphical model; likelihood ratio; multivariate data

Document Type: Research Article

Affiliations: 1: School of Mathematics and The Joseph Bell Centre for Forensic Statistics and Legal Reasoning, The King's Buildings, The University of Edinburgh, Mayfield Road, Edinburgh EH9 3JZ. 2: Institute of Forensic Research, Westerplatte 9, PL-31-033, Krakow, Poland.

Publication date: 2007-03-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more