Model-Based Classification via Mixtures of Multivariatet-Factor Analyzers

DOI10.1080/03610918.2011.595984zbMath1294.62142OpenAlexW1988586734WikidataQ108646946 ScholiaQ108646946MaRDI QIDQ4906432

Michelle A. Steane, Rickey Y. Yada, Paul D. McNicholas

Publication date: 11 February 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.595984

zbMATH Keywords

mixture modelsmodel-based classificationmultivariate \(t\)-distribution\(t\)-factor analyzers

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (16)

Parsimonious skew mixture models for model-based clustering and classification ⋮ Model-based clustering ⋮ The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers ⋮ Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures ⋮ Dimension reduction for model-based clustering via mixtures of multivariate \(t\)-distributions ⋮ Multivariate cluster weighted models using skewed distributions ⋮ Factor probabilistic distance clustering (FPDC): a new clustering method ⋮ The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture ⋮ Capturing patterns via parsimonious \(t\) mixture models ⋮ Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions ⋮ A mixture of generalized hyperbolic factor analyzers ⋮ A mixture of coalesced generalized hyperbolic distributions ⋮ Mixtures of generalized hyperbolic distributions and mixtures of skew-\(t\) distributions for model-based clustering with incomplete data ⋮ On fractionally-supervised classification: weight selection and extension to the multivariate \(t\)-distribution ⋮ Mixtures of hidden truncation hyperbolic factor analyzers ⋮ Discussion of `Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan

Uses Software

• mclust
• R

Cites Work

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• Model-based classification using latent Gaussian mixture models
• Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
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• Model-based classification via mixtures of multivariate \(t\)-distributions
• Mixtures of modified \(t\)-factor analyzers for model-based clustering, classification, and discriminant analysis
• Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
• Estimating the dimension of a model
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Variable Selection for Model-Based Clustering
• Using Unlabelled Data to Update Classification Rules with Applications in Food Authenticity Studies

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