Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data

Yuhong Wei, Yang Tang and Paul D. McNicholas

Computational Statistics & Data Analysis, 2019, vol. 130, issue C, 18-41

Abstract: Robust clustering from incomplete data is an important topic because, in many practical situations, real datasets are heavy-tailed, asymmetric, and/or have arbitrary patterns of missing observations. Flexible methods and algorithms for model-based clustering are presented via mixture of the generalized hyperbolic distributions and its limiting case, the mixture of multivariate skew-t distributions. An analytically feasible EM algorithm is formulated for parameter estimation and imputation of missing values for mixture models employing missing at random mechanisms. The proposed methodologies are investigated through a simulation study with varying proportions of synthetic missing values and illustrated using a real dataset. Comparisons are made with those obtained from the traditional mixture of generalized hyperbolic distribution counterparts by filling in the missing data using the mean imputation method.

Keywords: Clustering; Generalized hyperbolic; Missing data; Mixture models; Skew-t (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:130:y:2019:i:c:p:18-41

DOI: 10.1016/j.csda.2018.08.016

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