O'Hagan, AdrianAdrianO'HaganMurphy, Thomas BrendanThomas BrendanMurphyGormley, Isobel ClaireIsobel ClaireGormleyet al.2014-10-222014-10-222014 Elsev2016-01Computational Statistics and Data Analysishttp://hdl.handle.net/10197/6106Many 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.enThis is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis (VOL#, ISSUE#, (2014)) DOI: 10.1016/j.csda.2014.09.006Machine Learning & StatisticsModel-based clusteringMultivariate normal inverse Gaussian distributionMclustInformation metricsKolmogorov–Smirnov goodness of fitJournal Article93183010.1016/j.csda.2014.09.0062014-10-17https://creativecommons.org/licenses/by-nc-nd/3.0/ie/