Analytic calculations for the EM algorithm for multivariate skew- mixture models

A matrix variate skew‐t distribution

“…For example, some work has been done using symmetric component densities that parameterize concentration (tail weight), e.g., the t distribution , Andrews & McNicholas 2011, Lin, McNicholas & Hsiu 2014) and the power exponential distribution (Dang, Browne & McNicholas 2015). There has also been work on mixtures for discrete data (e.g., Karlis & Meligkotsidou 2007, Bouguila & ElGuebaly 2009) as well as several examples of mixtures of skewed distributions such as the NIG distribution (Karlis & Santourian 2009, Subedi & McNicholas 2014, the skew-t distribution (Lin 2010, Vrbik & McNicholas 2012, Lee & McLachlan 2014, 2016, the shifted asymmetric Laplace distribution (Morris & McNicholas 2013, Franczak, Browne & McNicholas 2014, the variance-gamma distribution , the generalized hyperbolic distribution , and others (e.g., Elguebaly & Bouguila 2015, Franczak, Tortora, Browne & McNicholas 2015.…”

An Improved Brain Mr Image Segmentation using Truncated Skew Gaussian Mixture

“…Notable distribution among such models include the skew normal mixture model [15][16], the skew t -mixture model [17][18] [19], the skew t -normal mixture model [20], and some other non-elliptical approaches [21][22] [23]. The log-Normal, the Burr, the Weibull the Gamma and the Generalized Pareto distribution are also considered in the literature for analyzing asymmetric data.…”

Uniform Cross-entropy Clustering

“…The mixture model-based clustering literature has focused on the development of mixture distributions with more flexible parametric components like split distributions [6,7], skew distributions [8][9][10] and some other non-elliptical approaches [11][12][13].…”