SIAM Journal on Scientific and Statistical Computing
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Inference in model-based cluster analysis
Statistics and Computing
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
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Bayesian analysis of mixture modelling using the multivariate t distribution
Statistics and Computing
Robust mixture modelling using multivariate t-distribution with missing information
Pattern Recognition Letters
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Maximum likelihood estimation for multivariate skew normal mixture models
Journal of Multivariate Analysis
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Computationally efficient learning of multivariate t mixture models with missing information
Computational Statistics
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
Extending mixtures of multivariate t-factor analyzers
Statistics and Computing
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented. Computationally flexible EM-type algorithms are developed for parameter estimation of these models under a missing at random (MAR) mechanism. For ease of computation and theoretical developments, two auxiliary indicator matrices are incorporated into the estimating procedure for exactly extracting the location of observed and missing components of each observation. Computational aspects related to the specification of starting values, convergence assessment and model choice are also discussed. The practical usefulness of the proposed methodology is illustrated with real data examples and a simulation study with varying proportions of missing values.