Abstract
We present an effective approach for modelling compositional data with large concentrations of zeros and several levels of variation, applied to a database of elemental compositions of forensic glass of various use types. The procedure consists of the following: (i) partitioning the data set in subsets characterised by the same pattern of presence/absence of chemical elements and (ii) fitting a Bayesian hierarchical model to the transformed compositions in each data subset. We derive expressions for the posterior predictive probability that newly observed fragments of glass are of a certain use type and for computing the evidential value of glass fragments relating to two competing propositions about their source. The model is assessed using cross-validation, and it performs well in both the classification and evidence evaluation tasks.
| Original language | English |
|---|---|
| Pages (from-to) | 96 - 108 |
| Number of pages | 13 |
| Journal | Journal of Chemometrics |
| Volume | 29 |
| Issue number | 2 |
| Early online date | 13 Oct 2014 |
| DOIs | |
| Publication status | Published - Feb 2015 |
Bibliographical note
Copyright © 2014 John Wiley & Sons, Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.Keywords
- Bayes factor
- Classification
- Evidence evaluation
- Forensic glass
- Markov chain Monte Carlo
