Original Article
A matrix variate skew‐t distribution
Abstract
Although there is ample work in the literature dealing with skewness in the multivariate setting, there is a relative paucity of work in the matrix variate paradigm. Such work is, for example, useful for modelling three‐way data. A matrix variate skew‐t distribution is derived based on a mean‐variance matrix normal mixture. An expectation‐conditional maximization algorithm is developed for parameter estimation. Simulated data are used for illustration. Copyright © 2017 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 10
- Michael P. B. Gallaugher, Paul D. McNicholas, Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers, Advanced Studies in Behaviormetrics and Data Science, 10.1007/978-981-15-2700-5_11, (177-196), (2020).
- Amir Rezaei, Fatemeh Yousefzadeh, Reinaldo B. Arellano-Valle, Scale and shape mixtures of matrix variate extended skew normal distributions, Journal of Multivariate Analysis, 10.1016/j.jmva.2020.104649, 179, (104649), (2020).
- Michael P.B. Gallaugher, Paul D. McNicholas, Three skewed matrix variate distributions, Statistics & Probability Letters, 10.1016/j.spl.2018.08.012, 145, (103-109), (2019).
- Michael P. B. Gallaugher, Paul D. McNicholas, Mixtures of skewed matrix variate bilinear factor analyzers, Advances in Data Analysis and Classification, 10.1007/s11634-019-00377-4, (2019).
- Paula M. Murray, Ryan P. Browne, Paul D. McNicholas, Mixtures of Hidden Truncation Hyperbolic Factor Analyzers, Journal of Classification, 10.1007/s00357-019-9309-y, (2019).
- Lingzhe Guo, Reza Modarres, Testing the equality of matrix distributions, Statistical Methods & Applications, 10.1007/s10260-019-00477-7, (2019).
- Volodymyr Melnykov, Xuwen Zhu, Studying crime trends in the USA over the years 2000–2012, Advances in Data Analysis and Classification, 10.1007/s11634-018-0326-1, 13, 1, (325-341), (2018).
- Michael P.B. Gallaugher, Paul D. McNicholas, Finite mixtures of skewed matrix variate distributions, Pattern Recognition, 10.1016/j.patcog.2018.02.025, 80, (83-93), (2018).
- Volodymyr Melnykov, Xuwen Zhu, On model-based clustering of skewed matrix data, Journal of Multivariate Analysis, 10.1016/j.jmva.2018.04.007, 167, (181-194), (2018).
- Paula M. Murray, Ryan P. Browne, Paul D. McNicholas, A mixture of SDB skew- t factor analyzers, Econometrics and Statistics, 10.1016/j.ecosta.2017.05.001, 3, (160-168), (2017).
