A composite Bayesian hierarchical model of compositional data with zeros

Napier, G., Neocleous, T. and Nobile, A. (2015) A composite Bayesian hierarchical model of compositional data with zeros. Journal of Chemometrics, 9(2), pp. 96-108. (doi: 10.1002/cem.2681)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Nobile, Dr Agostino and Neocleous, Dr Tereza and Napier, Dr Gary
Authors: Napier, G., Neocleous, T., and Nobile, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Research Group:Statistics
Journal Name:Journal of Chemometrics
ISSN (Online):1099-128X

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