Finite mixture modelling using the skew normal distribution
Tsung‐I Lin, Jack C. Lee, Shu Y. Yen · 2007
Abstract: Normal mixture models provide the most popular framework for mod-elling heterogeneity in a population with continuous outcomes arising in a variety of subclasses. In the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied prob-lems. In this article, we address the problem of analyzing a mixture of skew nor-mal distributions from the likelihood-based and Bayesian perspectives, respectively. Computational techniques using EM-type algorithms are employed for iteratively computing maximum likelihood estimates. Also, a fully Bayesian approach using the Markov chain Monte Carlo method is developed to carry out posterior analyses. Numerical results are illustrated through two examples. Key words and phrases: ECM algorithm, ECME algorithm, Fisher information, Markov chain Monte Carlo, maximum likelihood estimation, skew normal mixtures. 1.