The orthogonal skew model: computationally efficient multivariate skew-normal and skew-t distributions with applications to model-based clustering
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Ryan P. Browne [1] ; Jeffrey L. Andrews [2]
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[1] University of Waterloo
University of Waterloo
Canadá
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[2] University of British Columbia
University of British Columbia
Canadá
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 33, Nº. 3, 2024, págs. 752-785
- Idioma: inglés
- DOI: 10.1007/s11749-024-00920-2
- Texto completo no disponible (Saber más ...)
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Resumen
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.
