TY - JOUR
AU1 - Tortora, Cristina
AU2 - Franczak, Brian
AU3 - Browne, Ryan
AU4 - McNicholas, Paul
AB - A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is intro- duced. 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 imple- mentation 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. Keywords Clustering · Coalesced distributions · Convexity · Finite mixture models · Generalized hyperbolic distribution · Mixture of mixtures · MM algorithm · Multiple scaled distributions Paul D. McNicholas mcnicholas@math.mcmaster.ca Cristina Tortora cristina.tortora@sjsu.edu Brian C. Franczak franczakb@macewan.ca Ryan P. Browne rpbrowne@uwaterloo.ca Department of Mathematics & Statistics, San Jose ´ State University, San Jose, ´ CA, USA Department of Mathematics & Statistics, MacEwan University, Edmonton, AB, Canada Department 
TI - A Mixture of Coalesced Generalized Hyperbolic Distributions
JF - Journal of Classification
DO - 10.1007/s00357-019-09319-3
DA - 2019-04-22
UR - https://www.deepdyve.com/lp/springer-journals/a-mixture-of-coalesced-generalized-hyperbolic-distributions-TWU4PS00RR
SP - 1
EP - 32
VL - OnlineFirst
IS - 
DP - DeepDyve
ER -