Alhaji, BB and Dai, H and Hayashi, Y and Vinciotti, V and Harrison, A and Lausen, B (2016) 'Bayesian analysis for mixtures of discrete distributions with a non-parametric component.' Journal of Applied Statistics, 43 (8). 1369 - 1385. ISSN 0266-4763

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Abstract

© 2015 Taylor & Francis. 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
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User:	Hongsheng Dai
Date Deposited:	24 Sep 2015 13:53
Last Modified:	22 Jan 2019 20:15
URI:	http://repository.essex.ac.uk/id/eprint/15038

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