Clustering with the multivariate normal inverse Gaussian distribution

Files in This Item:
 File SizeFormat
Downloadinsight_publication.pdf20.02 MBAdobe PDF
Title: Clustering with the multivariate normal inverse Gaussian distribution
Authors: O'Hagan, AdrianMurphy, Thomas BrendanGormley, Isobel Claireet al.
Permanent link:
Date: Jan-2016
Online since: 2014-10-22T14:02:50Z
Abstract: Many model-based clustering methods are based on a finite Gaussian mixture model. The Gaussian mixture model implies that the data scatter within each group is elliptically shaped. Hence non-elliptical groups are often modeled by more than one component, resulting in model over-fitting. An alternative is to use a mean–variance mixture of multivariate normal distributions with an inverse Gaussian mixing distribution (MNIG) in place of the Gaussian distribution, to yield a more flexible family of distributions. Under this model the component distributions may be skewed and have fatter tails than the Gaussian distribution. The MNIG based approach is extended to include a broad range of eigendecomposed covariance structures. Furthermore, MNIG models where the other distributional parameters are constrained is considered. The Bayesian Information Criterion is used to identify the optimal model and number of mixture components. The method is demonstrated on three sample data sets and a novel variation on the univariate Kolmogorov–Smirnov test is used to assess goodness of fit.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Journal Article
Publisher: Elsevier
Journal: Computational Statistics and Data Analysis
Volume: 93
Start page: 18
End page: 30
Copyright (published version): 2014 Elsevier
Keywords: Machine Learning & StatisticsModel-based clusteringMultivariate normal inverse Gaussian distributionMclustInformation metricsKolmogorov–Smirnov goodness of fit
DOI: 10.1016/j.csda.2014.09.006
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

Show full item record

Citations 10

Last Week
Last month
checked on Sep 11, 2020

Page view(s) 50

Last Week
Last month
checked on Dec 8, 2022

Download(s) 1

checked on Dec 8, 2022

Google ScholarTM



If you are a publisher or author and have copyright concerns for any item, please email and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.