A mixture of generalized hyperbolic factor analyzers

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• 作者：Cristina Tortora ; Paul D. McNicholas…
• 关键词：Clustering ; Generalized hyperbolic distribution ; Mixture of factor analyzers ; AECM algorithm
• 刊名：Advances in Data Analysis and Classification
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：10
• 期：4
• 页码：423-440
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistical Theory and Methods; Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Life Sciences, Medicine, Health Sciences; Statistics for Engineering, Physics, Computer
• 出版者：Springer Berlin Heidelberg
• ISSN：1862-5355
• 卷排序：10

文摘

The mixture of factor analyzers model, which has been used successfully for the model-based clustering of high-dimensional data, is extended to generalized hyperbolic mixtures. The development of a mixture of generalized hyperbolic factor analyzers is outlined, drawing upon the relationship with the generalized inverse Gaussian distribution. An alternating expectation-conditional maximization algorithm is used for parameter estimation, and the Bayesian information criterion is used to select the number of factors as well as the number of components. The performance of our generalized hyperbolic factor analyzers model is illustrated on real and simulated data, where it performs favourably compared to its Gaussian analogue and other approaches.