NDLI: A mixture of generalized hyperbolic factor analyzers

Content Provider	Springer Nature Link
Author	Tortora, Cristina McNicholas, Paul D. Browne, Ryan P.
Copyright Year	2015
Abstract	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.
Starting Page	423
Ending Page	440
Page Count	18
File Format	PDF
ISSN	18625347
Journal	Advances in Data Analysis and Classification
Volume Number	10
Issue Number	4
e-ISSN	18625355
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-04-07
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Generalized hyperbolic distribution Mixture of factor analyzers AECM algorithm Classification and discrimination; cluster analysis Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Life Sciences, Medicine, Health Sciences Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Data Mining and Knowledge Discovery
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Computer Science Applications

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