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Journal articles
A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweights: Application to robust clustering
Abstract : 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 ttails. For these latter cases, we provide maximum likelihood estimation of the parameters and illustrate their modelling flexibility on simulated and real data clustering example.
Cited literature [16 references]
https://hal.inria.fr/hal-00823451
Contributor : Florence Forbes <>
Submitted on : Wednesday, July 24, 2013 - 10:48:01 AM
Last modification on : Monday, July 20, 2020 - 9:16:02 AM
Document(s) archivé(s) le : Friday, October 25, 2013 - 4:10:13 AM
Contributor : Florence Forbes <>
Submitted on : Wednesday, July 24, 2013 - 10:48:01 AM
Last modification on : Monday, July 20, 2020 - 9:16:02 AM
Document(s) archivé(s) le : Friday, October 25, 2013 - 4:10:13 AM
