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Bayesian analysis for mixtures of discrete distributions with a non-parametric component

Alhaji, Baba B and Dai, Hongsheng and Hayashi, Yoshiko and Vinciotti, Veronica and Harrison, Andrew and Lausen, Berthold (2016) 'Bayesian analysis for mixtures of discrete distributions with a non-parametric component.' Journal of Applied Statistics, 43 (8). pp. 1369-1385. ISSN 0266-4763

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Bayesian finite mixture modelling is a flexible parametric modelling approach for classification and density fitting. Many areas of application require distinguishing a signal from a noise component. In practice, it is often difficult to justify a specific distribution for the signal component; therefore, the signal distribution is usually further modelled via a mixture of distributions. However, modelling the signal as a mixture of distributions is computationally non-trivial due to the difficulties in justifying the exact number of components to be used and due to the label switching problem. This paper proposes the use of a non-parametric distribution to model the signal component. We consider the case of discrete data and show how this new methodology leads to more accurate parameter estimation and smaller false non-discovery rate. Moreover, it does not incur the label switching problem. We show an application of the method to data generated by ChIP-sequencing experiments.

Item Type: Article
Uncontrolled Keywords: Bayesian; label switching; mixture model; Gibbs sampler
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Sep 2015 13:53
Last Modified: 15 Jan 2022 00:28

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