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Model-based clustering and classification with non-normal mixture distributions

Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan

Comments on: model-based clustering and classification with non-normal mixture distributions

Discussion of “Model-based clustering and classification with non-normal mixture distributions” by S.X. Lee and G.J. McLachlan

Discussion of ‘Model-based clustering and classification with non-normal mixture distributions’ by Lee and McLachlan

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Model-based clustering and classification with non-normal mixture distributions

Content Provider	Springer Nature Link
Author	Lee, Sharon X. McLachlan, Geoffrey J.
Copyright Year	2013
Abstract	Non-normal mixture distributions have received increasing attention in recent years. Finite mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and skew $$t$$ -mixture models, are emerging as promising extensions to the traditional normal and $$t$$ -mixture models. Most of these parametric families of skew distributions are closely related, and can be classified into four forms under a recently proposed scheme, namely, the restricted, unrestricted, extended, and generalised forms. In this paper, we consider some of these existing proposals of multivariate non-normal mixture models and illustrate their practical use in several real applications. We first discuss the characterizations along with a brief account of some distributions belonging to the above classification scheme, then references for software implementation of EM-type algorithms for the estimation of the model parameters are given. We then compare the relative performance of restricted and unrestricted skew mixture models in clustering, discriminant analysis, and density estimation on six real datasets from flow cytometry, finance, and image analysis. We also compare the performance of mixtures of skew normal and $$t$$ -component distributions with other non-normal component distributions, including mixtures with multivariate normal-inverse-Gaussian distributions, shifted asymmetric Laplace distributions and generalized hyperbolic distributions.
Starting Page	427
Ending Page	454
Page Count	28
File Format	PDF
ISSN	16182510
Journal	Statistical Methods and Applications
Volume Number	22
Issue Number	4
e-ISSN	1613981X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-07-31
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mixture models Skew distributions Multivariate skew normal distribution Multivariate skew $$t$$ -distribution EM algorithm Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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