Parsimonious skew mixture models for model-based clustering and classification

Authors:
Irene Vrbik;Paul D. Mcnicholas
Affiliations:
-;-
Venue:
Computational Statistics & Data Analysis
Year:
2014

Citing 19
Cited 1

Multivariate statistics: a practical approach

Multivariate statistics: a practical approach
Deterministic annealing EM algorithm

Neural Networks
Mixtures of probabilistic principal component analyzers

Neural Computation
Robust mixture modelling using the t distribution

Statistics and Computing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of Factor Analyzers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data

DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based classification via mixtures of multivariate t-distributions

Computational Statistics & Data Analysis
Model-based clustering of microarray expression data via latent Gaussian mixture models

Bioinformatics
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data

Bioinformatics
Extending mixtures of multivariate t-factor analyzers

Statistics and Computing
Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Statistics and Computing
Multivariate mixture modeling using skew-normal independent distributions

Computational Statistics & Data Analysis
Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions

Statistics and Computing

Editorial: The 2nd special issue on advances in mixture models

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skew-t and skew-normal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to existing models in both unsupervised and semi-supervised classification frameworks. Parameter estimation is carried out using the expectation-maximization algorithm and models are selected using the Bayesian information criterion. The efficacy of these extensions is illustrated on simulated and real data sets.