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Journal of Classification
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22-04-2019

A Mixture of Coalesced Generalized Hyperbolic Distributions

Authors: Cristina Tortora, Brian C. Franczak, Ryan P. Browne, Paul D. McNicholas

Published in: Journal of Classification | Issue 1/2019

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Abstract

A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced. Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a generalized hyperbolic distribution with a multiple scaled generalized hyperbolic distribution. After detailing the development of the MMSGHDs, which arises via implementation of a multi-dimensional weight function, the density of the mixture of CGHDs is developed. A parameter estimation scheme is developed using the ever-expanding class of MM algorithms and the Bayesian information criterion is used for model selection. The issue of cluster convexity is examined and a special case of the MMSGHDs is developed that is guaranteed to have convex clusters. These approaches are illustrated and compared using simulated and real data. The identifiability of the MMSGHDs and the mixture of CGHDs are discussed in an appendix.
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Appendix
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Literature
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Metadata
Title
A Mixture of Coalesced Generalized Hyperbolic Distributions
Authors
Cristina Tortora
Brian C. Franczak
Ryan P. Browne
Paul D. McNicholas
Publication date
22-04-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09319-3

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A New Method for Classifying Random Variables Based on Support Vector Machine

Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering

Multiclass Classification Based on Multi-criteria Decision-making

Growth Mixture Modeling with Measurement Selection

Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

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