Sharon X. Lee

TL;DR: Comparisons are presented to illustrate the relative performance of the restricted and unrestricted models, and demonstrate the usefulness of the recently proposed methodology for the unrestricted MST mixture, by some applications to three real datasets.

TL;DR: A systematic classification of the existing skew symmetric distributions into four types is presented, thereby clarifying their close relationships and aiding in understanding the link between some of the proposed expectation-maximization based algorithms for the computation of the maximum likelihood estimates of the parameters of the models.

TL;DR: An algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions and an iterative algorithm for the computation of the ML estimates of its model parameters, presented with the package EMMIXuskew.

TL;DR: Lee and McLachlan as mentioned in this paper introduced a finite mixture of canonical fundamental skew $$t$$t (CFUST) distributions for a model-based approach to clustering where the clusters are asymmetric and possibly long-tailed.

TL;DR: A consensus was reached that a novel classification scheme for medulloblastoma based on the four molecular subgroups, as well as histopathologic features, should be presented for consideration in the upcoming fifth edition of the World Health Organization’s classification of tumours of the central nervous system.