Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37074
標題: Finite mixture modelling using the skew normal distribution
作者: Lin, T.I.
林宗儀
Lee, J.C.
Yen, S.Y.
關鍵字: ECM algorithm
ECME algorithm
fisher information
Markov chain Monte
Carlo
maximum likelihood estimation
skew normal mixtures
t-distribution
maximum-likelihood
bayesian-analysis
unknown number
em algorithm
convergence
components
extension
ecm
期刊/報告no:: Statistica Sinica, Volume 17, Issue 3, Page(s) 909-927.
摘要: 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. In this article, we address the problem of analyzing a mixture of skew normal 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.
URI: http://hdl.handle.net/11455/37074
ISSN: 1017-0405
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