Robust clustering via mixtures of \(t\) factor analyzers with incomplete data
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Publication:2103856
DOI10.1007/s11634-021-00453-8OpenAlexW3177921858MaRDI QIDQ2103856
Publication date: 9 December 2022
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11634-021-00453-8
information matrixdata reductionmissing datamixture modelsmultivariate \(t\) distributionfactor analyzer
Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
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Cites Work
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- Modelling high-dimensional data by mixtures of factor analyzers
- Maximum likelihood inference for the multivariate \(t\) mixture model
- Extending mixtures of factor models using the restricted multivariate skew-normal distribution
- Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
- Estimating the dimension of a model
- Efficient ML estimation of the multivariate normal distribution from incomplete data
- Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation
- Automated learning of factor analysis with complete and incomplete data
- Maximum likelihood estimation of mixtures of factor analyzers
- Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions
- Robust clustering of multiply censored data via mixtures of \(t\) factor analyzers
- An efficient ECM algorithm for maximum likelihood estimation in mixtures of \(t\)-factor analyzers
- A flexible class of parametric distributions for Bayesian linear mixed models
- Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework
- Model-based clustering of censored data via mixtures of factor analyzers
- Automated learning of \(t\) factor analysis models with complete and incomplete data
- Capturing patterns via parsimonious \(t\) mixture models
- On fast supervised learning for normal mixture models with missing information
- Automated learning of mixtures of factor analysis models with missing information
- Bayesian Analysis of Mixtures of Factor Analyzers
- Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
- Maximum likelihood estimation via the ECM algorithm: A general framework
- Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
- Mixture Densities, Maximum Likelihood and the EM Algorithm
- Inference and missing data
- Multivariate T-Distributions and Their Applications
- Full information maximum likelihood estimation in factor analysis with a large number of missing values
- Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers
