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DC Field | Value | Language |
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dc.contributor.author | Alhaji, BB | - |
dc.contributor.author | Dai, H | - |
dc.contributor.author | Hayashi, Y | - |
dc.contributor.author | Vinciotti, V | - |
dc.contributor.author | Harrison, A | - |
dc.contributor.author | Lausen, B | - |
dc.date.accessioned | 2015-11-16T15:33:55Z | - |
dc.date.available | 2015-11-16T15:33:55Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Journal of Applied Statistics, 2015 | en_US |
dc.identifier.issn | 1360-0532 | - |
dc.identifier.uri | http://www.tandfonline.com/doi/full/10.1080/02664763.2015.1100594 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/11603 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis (Routledge) | en_US |
dc.subject | Bayesian | en_US |
dc.subject | Label switching | en_US |
dc.subject | Mixture model | en_US |
dc.subject | Gibbs sampler | en_US |
dc.title | Bayesian analysis for mixtures of discrete distributions with a non-parametric component | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1080/02664763.2015.1100594 | - |
dc.relation.isPartOf | Journal of Applied Statistics | - |
pubs.publication-status | Accepted | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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Fulltext.pdf | 317.87 kB | Adobe PDF | View/Open |
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