A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering
Forbes, Florence & Wraith, Darren (2014) A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: Application to robust clustering. Statistics and Computing, 24(6), pp. 971-984.
We propose a family of multivariate heavy-tailed distributions that allow variable marginal amounts of tailweight. The originality comes from introducing multidimensional instead of univariate scale variables for the mixture of scaled Gaussian family of distributions. In contrast to most existing approaches, the derived distributions can account for a variety of shapes and have a simple tractable form with a closed-form probability density function whatever the dimension. We examine a number of properties of these distributions and illustrate them in the particular case of Pearson type VII and t tails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering examples.
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|Item Type:||Journal Article|
|Keywords:||Covariance matrix decomposition, EM algorithm, Gaussian scale mixture, Multivariate generalized t -distribution, Outlier detection|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2013 Springer Science+Business Media New York|
|Deposited On:||04 Feb 2016 05:54|
|Last Modified:||05 Feb 2016 01:37|
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