Scale and shape mixtures of multivariate skew-normal distributions
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Article
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-02-26Online Publication Date
2018-02-26Print Publication Date
2018-07Permanent link to this record
http://hdl.handle.net/10754/627215Metadata
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We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.Citation
Arellano-Valle RB, Ferreira CS, Genton MG (2018) Scale and shape mixtures of multivariate skew-normal distributions. Journal of Multivariate Analysis. Available: http://dx.doi.org/10.1016/j.jmva.2018.02.007.Sponsors
This research was supported by Fondecyt (Chile)1120121 and 1150325, and by the King Abdullah University of Science and Technology (KAUST) . We thank the Editor, Associate Editor and four anonymous reviewers for comments that improved the paper. We also thank Prof. Adelchi Azzalini for suggesting Proposition 1 during a seminar presentation of this work at the University of Padova and Prof. Mauricio Castro for some initial discussions on the topic of this paper.Publisher
Elsevier BVJournal
Journal of Multivariate AnalysisAdditional Links
http://www.sciencedirect.com/science/article/pii/S0047259X17303937Scopus Count
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