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DC Field | Value | Language |
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dc.contributor.author | Tang, ML | - |
dc.contributor.author | Wu, Q | - |
dc.contributor.author | Yang, S | - |
dc.contributor.author | Tian, GL | - |
dc.date.accessioned | 2022-01-08T18:28:19Z | - |
dc.date.available | 2022-01-08T18:28:19Z | - |
dc.date.issued | 2021-12-16 | - |
dc.identifier.citation | Tang, M.L., Wu, Q., Yang, S. and Tian, G.L. (2021) 'Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome', Biometrical Journal, 0 (in press), pp. 1-19. doi: 10.1002/bimj.202000334. | en_US |
dc.identifier.issn | 0323-3847 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23914 | - |
dc.description.abstract | Copyright © 2021 The Authors. Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China. Grant Numbers: 12171167, 11801184; Research Grant Council of the Hong Kong Special Administrative Region. Grant Numbers: UGC/FDS14/P06/17, UGC/FDS14/P02/18. | en_US |
dc.format.extent | 1 - 19 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Wiley-VCH GmbH. | en_US |
dc.rights | © 2021 The Authors. Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | compositional data | en_US |
dc.subject | Dirichlet distribution | en_US |
dc.subject | EM algorithm | en_US |
dc.subject | essential zero | en_US |
dc.subject | gamma distribution | en_US |
dc.subject | rounded zeros | en_US |
dc.subject | stochastic representation | en_US |
dc.title | Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1002/bimj.202000334 | - |
dc.relation.isPartOf | Biometrical Journal | - |
pubs.issue | 0 | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1521-4036 | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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