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標題: Finite mixture modelling using the skew normal distribution
作者: Lin, T.I.;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
日期: 2007
Issue Date: 2012-12-14 10:05:59 (UTC+8)
關連: 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.
Relation: Statistica Sinica
Appears in Collections:[依資料類型分類] 期刊論文
[依教師分類] 林宗儀
[依教師分類] 林宗儀

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