Clustering gene expression time course data using mixtures of multivariate \(t\)-distributions

DOI10.1016/j.jspi.2011.11.026zbMath1236.62068OpenAlexW1988261662MaRDI QIDQ413359

Sanjeena Subedi, Paul D. McNicholas

Publication date: 4 May 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2011.11.026

zbMATH Keywords

model-based clusteringCholesky decomposition

Mathematics Subject Classification ID

Related Items (15)

Model-based clustering via linear cluster-weighted models ⋮ Parsimonious skew mixture models for model-based clustering and classification ⋮ Mixtures of skew-\(t\) factor analyzers ⋮ Model-based clustering ⋮ Dimension reduction for model-based clustering via mixtures of multivariate \(t\)-distributions ⋮ The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture ⋮ clusterMLD: An Efficient Hierarchical Clustering Method for Multivariate Longitudinal Data ⋮ Clustering and classification via cluster-weighted factor analyzers ⋮ Capturing patterns via parsimonious \(t\) mixture models ⋮ longclust ⋮ Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions ⋮ Growth mixture modeling with measurement selection ⋮ Flexible mixture modelling with the polynomial Gaussian cluster-weighted model ⋮ Mixtures of multivariate contaminated normal regression models ⋮ Discussion of `Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan

Uses Software

• PGMM

Cites Work

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• Extending mixtures of multivariate \(t\)-factor analyzers

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