Unsupervised learning via mixtures of skewed distributions with hypercube contours

Mixture models whose components have skewed hypercube contours are developed via a generalization of the multivariate shifted asymmetric Laplace density. Specifically, we develop mixtures of multiple scaled shifted asymmetric Laplace distributions. The component densities have a unique combination of features: they include a multivariate weight function and the marginal distributions are asymmetric Laplace. We use these mixtures of multiple …