文章基本信息

标题：Finite Mixture Modeling via Skew-Laplace Birnbaum–Saunders Distribution
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作者：Mehrdad Naderi ; Mahdieh Mozafari ; Kheirolah Okhli 等
期刊名称：Journal of Statistical Theory and Applications (JSTA)
电子版ISSN：1538-7887
出版年度：2020
卷号：19
期号：1
页码：49-58
DOI：10.2991/jsta.d.200224.008
语种：English
出版社：Atlantis Press
摘要：Finite mixture model is a widely acknowledged model-based clustering method for analyzing data. In this paper, a new finite mixture model via an extension of Birnbaum–Saunders distribution is introduced. The new mixture model provide a useful generalization of the heavy-tailed lifetime model since the mixing components cover both skewness and kurtosis. Some properties and characteristics of the model are derived and an expectation and maximization (EM)-type algorithm is developed to compute maximum likelihood estimates. The asymptotic standard errors of the parameter estimates are obtained via offering an information-based approach. Finally, the performance of the methodology is illustrated by considering both simulated and real datasets.
关键词：Birnbaum–Saunders distribution; Normal mean-variance mixture model; Skew-Laplace distribution; Finite mixture model; ECM algorithm