Robust mixture modelling using the t distribution
Statistics and Computing
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Parsimonious Gaussian mixture models
Statistics and Computing
Model-based clustering with non-elliptically contoured distributions
Statistics and Computing
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Statistics and Computing
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
Model-based classification via mixtures of multivariate t-distributions
Computational Statistics & Data Analysis
Dimension reduction for model-based clustering
Statistics and Computing
Extending mixtures of multivariate t-factor analyzers
Statistics and Computing
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace
Statistics and Computing
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We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of $$t$$t-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The method relies on identifying a reduced subspace of the data by considering the extent to which group means and group covariances vary. This subspace contains linear combinations of the original data, which are ordered by importance via the associated eigenvalues. Observations can be projected onto the subspace and the resulting set of variables captures most of the clustering structure available in the data. The approach is illustrated using simulated and real data, where it outperforms its Gaussian analogue.