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Robust mixture modeling using the skew t distribution
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Advances in Data Analysis and Classification
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Computational Statistics & Data Analysis
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
Computational Statistics & Data Analysis
Multivariate measurement error models using finite mixtures of skew-Student t distributions
Journal of Multivariate Analysis
Finite mixtures of multivariate skew t-distributions: some recent and new results
Statistics and Computing
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This paper presents a robust mixture modeling framework using the multivariate skew t distributions, an extension of the multivariate Student's t family with additional shape parameters to regulate skewness. The proposed model results in a very complicated likelihood. Two variants of Monte Carlo EM algorithms are developed to carry out maximum likelihood estimation of mixture parameters. In addition, we offer a general information-based method for obtaining the asymptotic covariance matrix of maximum likelihood estimates. Some practical issues including the selection of starting values as well as the stopping criterion are also discussed. The proposed methodology is applied to a subset of the Australian Institute of Sport data for illustration.