The orthogonal skew model: computationally efficient multivariate skew-normal and skew-t distributions with applications to model-based clustering

Ryan P. Browne () and Jeffrey L. Andrews ()
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Ryan P. Browne: University of Waterloo
Jeffrey L. Andrews: University of British Columbia

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2024, vol. 33, issue 3, No 10, 752-785

Abstract: Abstract We introduce a parameterization for the multivariate skew normal and skew-t distributions, which enforces an orthogonal structure on the skewness parameter. This approach provides substantial benefits in computational efficiency during parameter estimation, resulting in a model which strikes an excellent balance between flexibility and model-fitting feasibility. We illustrate this primarily through implementing the proposed distributions in a mixture model-based clustering framework. We compare to competing skew distributions via both simulated and real data analyses, reporting both computation time and model-fit metrics.

Keywords: Model based clustering; Skew-normal; Skew-t; 62H30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11749-024-00920-2

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