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Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters

Zhang, Jian (2013) Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters. Journal of the American Statistical Association, 108 (504). pp. 1366-1384. ISSN 0162-1459. (doi:10.1080/01621459.2013.835661) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:41701)

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http://dx.doi.org/10.1080/01621459.2013.835661

Abstract

Most clustering methods assume that the data can be represented by mutually exclusive clusters, although this assumption may not be the

in a cell and may, therefore, belong to more than one cluster simultaneously, and that gene clusters can be linked to each other in certain

and simulation studies, for which the epistatic structures were known in advance, and on real data analyses. To explore potential links

expectation-maximization (EM) algorithm is developed to compute the related maximum likelihood estimators. The Bayesian information

model is a significantly better fit to the data than a standard mixture model in which each data point belongs to one cluster. The asymptotic

epistatic links between clusters do have a serious effect on the accuracy of clustering and that our epistatic approach can substantially reduce

such an effect and improve the fit.

Item Type: Article
DOI/Identification number: 10.1080/01621459.2013.835661
Uncontrolled keywords: Asymptotic property; Bootstrap test; Epistatic link; Finite epistatic mixture; Generalized EM algorithm; Model-based epistatic clustering.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 07 Jul 2014 14:01 UTC
Last Modified: 13 Feb 2020 04:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41701 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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