Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions

Redivo, E., Nguyen, H. and Gupta, M. (2020) Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions. Computational Statistics and Data Analysis, 152, 107040. (doi: 10.1016/j.csda.2020.107040)

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Model-based clustering approaches generally assume that the observations to be clustered are generated from a mixture of distributions, each component of the mixture corresponding to a particular parametric distribution. Most commonly, the underlying distribution is assumed to be normal, which is inadequate for many situations, for example when skewness or multimodality is present within the components. The problem is intensified when the data dimension increases, leading to inaccurate groupings and incorrect inference. A new Bayesian model-based clustering approach is proposed, that can handle a variety of complexities in the data, based on a recently introduced family of geometric skew normal distributions. The performance of this methodology is illustrated through a number of simulation studies and applications to a number of datasets from genomics and medicine.

Item Type:Articles
Additional Information:Hien Nguyen is supported by the Australian Research Council grants DE170101134 and DP180101192.
Glasgow Author(s) Enlighten ID:Gupta, Dr Mayetri and Redivo, Mr Edoardo
Authors: Redivo, E., Nguyen, H., and Gupta, M.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Computational Statistics and Data Analysis
ISSN (Online):1872-7352
Published Online:25 June 2020
Copyright Holders:Copyright © 2020 Elsevier
First Published:First published in Computational Statistics and Data Analysis 152:107040
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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