Parsimonious Skew Mixture Models for Model-Based Clustering and Classification
Methodology
2013-11-12 v1
Abstract
In recent work, robust mixture modelling approaches using skewed distributions have been explored to accommodate asymmetric data. We introduce parsimony by developing skew-t and skew-normal analogues of the popular GPCM family that employ an eigenvalue decomposition of a positive-semidefinite matrix. The methods developed in this paper are compared to existing models in both an unsupervised and semi-supervised classification framework. Parameter estimation is carried out using the expectation-maximization algorithm and models are selected using the Bayesian information criterion. The efficacy of these extensions is illustrated on simulated and benchmark clustering data sets.
Cite
@article{arxiv.1302.2373,
title = {Parsimonious Skew Mixture Models for Model-Based Clustering and Classification},
author = {Irene Vrbik and Paul D. McNicholas},
journal= {arXiv preprint arXiv:1302.2373},
year = {2013}
}