Learning from incomplete data via parameterized \(t\) mixture models through eigenvalue decomposition

DOI10.1016/j.csda.2013.02.020zbMath1471.62120OpenAlexW2022971702MaRDI QIDQ1621293

Tsung I. Lin

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.02.020

zbMATH Keywords

model-based clusteringintegrated completed likelihoodEM-type algorithmseigenvalue decompositionF-G algorithmmultivariate \(t\) mixture models

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)

Related Items

Editorial: The 2nd special issue on advances in mixture models ⋮ Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values ⋮ Multivariate cluster-weighted models based on seemingly unrelated linear regression ⋮ Maximum likelihood inference for the multivariate \(t\) mixture model ⋮ Identifying connected components in Gaussian finite mixture models for clustering ⋮ Location and scale mixtures of Gaussians with flexible tail behaviour: properties, inference and application to multivariate clustering ⋮ Mixture model selection via hierarchical BIC ⋮ The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture ⋮ Robust model-based clustering via mixtures of skew-\(t\) distributions with missing information ⋮ Finite mixture of censored linear mixed models for irregularly observed longitudinal data ⋮ Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions ⋮ Mixtures of restricted skew-\(t\) factor analyzers with common factor loadings ⋮ Model-based clustering and outlier detection with missing data ⋮ Finite mixture modeling of censored data using the multivariate Student-\(t\) distribution ⋮ Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions ⋮ Unconstrained representation of orthogonal matrices with application to common principal components ⋮ Mixtures of generalized hyperbolic distributions and mixtures of skew-\(t\) distributions for model-based clustering with incomplete data

Uses Software

• mclust
• R
• UCI-ml
• Mixmod
• PRMLT

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