Statistical analysis with missing data
Statistical analysis with missing data
Algorithms for clustering data
Algorithms for clustering data
ML estimation of the multivariate t distribution and the EM algorithm
Journal of Multivariate Analysis
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Robust mixture modelling using the t distribution
Statistics and Computing
Mixture model clustering for mixed data with missing information
Computational Statistics & Data Analysis
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Robust mixture modeling using the skew t distribution
Statistics and Computing
Robust Nonparametric Probability Density Estimation by Soft Clustering
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Analyzing human gaze path during an interactive optimization task
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
Robust speaker identification based on t-distribution mixture model
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
Computational Statistics & Data Analysis
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Multivariate mixture modeling using skew-normal independent distributions
Computational Statistics & Data Analysis
Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition
Computational Statistics & Data Analysis
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Modelling mixtures of multivariate t-distributions are usually used instead of Gaussian mixture models as a robust approach, when one fits a set of continuous multivariate data which have wider tail than Gaussian's or atypical observations. Further, the multivariate data set often involves missing values, which cannot be circumvented and then the missing values must be handled properly. In this paper, we present a framework for fitting mixtures of multivariate t-distributions when data are missing at random on the basis of maximum likelihood estimation. We resort to EM algorithm both for the estimation of mixture components and for coping with missing values. The iterative algorithm obtained can be applied to an extensive range of unsupervised clustering as well as supervised discrimination.