Finite mixture modelling using the skew normal distribution. (English) Zbl 1133.62012

Summary: Normal mixture models provide the most popular framework for modelling 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 problems. We address the problem of analyzing a mixture of skew normal distributions from 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.


62F10 Point estimation
62F15 Bayesian inference
65C60 Computational problems in statistics (MSC2010)
65C40 Numerical analysis or methods applied to Markov chains