2,933 research outputs found

    Bayes Estimation for the Mean Matrix of the SSMESN Family of Matrix Variate Distributions

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    In this paper, the problem of finding a Bayes estimation for the mean matrix of the scale and shape mixtures of matrix variate extended skew normal distributions is considered, and its applications in the multivariate linear regression and the stress-strength models are described. Finally, a simulation study and a real data analysis are presented for applications

    Mixtures of Common Skew-t Factor Analyzers

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    A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well-modelled by the mixtures of factor analyzers model or a variant thereof. Furthermore, assuming that the component densities follow a skew-t distribution allows robust clustering of skewed data. The alternating expectation-conditional maximization algorithm is employed for parameter estimation. We demonstrate excellent clustering performance when our model is applied to real and simulated data.This paper marks the first time that skewed common factors have been used
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