A composite Bayesian hierarchical model of compositional data with zeros

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JournalJournal of Chemometrics
DateE-pub ahead of print - 13 Oct 2014
DatePublished (current) - Feb 2015
Issue number2
Volume29
Number of pages13
Pages (from-to)96 - 108
Early online date13/10/14
Original languageEnglish

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.

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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.

    Research areas

  • Bayes factor, Classification, Evidence evaluation, Forensic glass, Markov chain Monte Carlo

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