A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis
Variable selection in model-based clustering: A general variable role modeling
Computational Statistics & Data Analysis
Characterizing the fine structure of a neural sensory code through information distortion
Journal of Computational Neuroscience
Variable selection in model-based discriminant analysis
Journal of Multivariate Analysis
Slope heuristics: overview and implementation
Statistics and Computing
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
An infinite mixture of inverted dirichlet distributions
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
EM algorithms for multivariate Gaussian mixture models with truncated and censored data
Computational Statistics & Data Analysis
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
A multivariate linear regression analysis using finite mixtures of t 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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The Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM), and it is possible to combine these to yield different strategies for obtaining a sensible maximum for the likelihood (or complete-data likelihood) function. MIXMOD is currently intended to be used for multivariate Gaussian mixtures, and fourteen different Gaussian models can be distinguished according to different assumptions regarding the component variance matrix eigenvalue decomposition. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance) are included, their suitability depending on the particular perspective (cluster analysis or discriminant analysis). Written in C++, MIXMOD is interfaced with SCILAB and MATLAB. The program, the statistical documentation and the user guide are available on the internet at the following address: http://www-math.univ-fcomte.fr/mixmod/index.php.