Finite mixture modelling using the skew normal distribution

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Model-based clustering of high-dimensional data: A review (2014, background): “Let us however notice that several recent works focused on diﬀerent distributions such as the skew normal [52], asymmetric Laplace [36] or t -distributions [3, 51, 68].”
A Skew-Normal Mixture Regression Model (2014, unknown): “By Bayes’s rule, it suffices to show that gj Y = yð Þ;TN du, 1 d2; 0, ‘ð Þ : ðA4Þ 156 Educational and Psychological Measurement 74(1) By Lemma 2 of Lin, Lee, and Yen (2007), the first two moments of (A4) are given by E gjyð Þ= du + 1 d2 1=2 f luð Þ F luð Þ , ðA5Þ E g2jy = 1 d2 + duE gjyð Þ ðA6Þ 2.”
Inference in finite state space non parametric Hidden Markov Models and applications (2015, background): “In the recent years, big efforts have been made to consider more ﬂexible parametric emission distributions (see e.g. Lin et al. 2007).”
Finite mixture models and model-based clustering (2010, methodology): “Finite mixtures (of univariate skew-normal distributions) were first analyzed by [71].”