Mixtures of skewed matrix variate bilinear factor analyzers

DOI10.1007/s11634-019-00377-4zbMath1474.62227arXiv1809.02385OpenAlexW2990101034MaRDI QIDQ2201326

Michael P. B. Gallaugher, Paul D. McNicholas

Publication date: 29 September 2020

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.02385

zbMATH Keywords

clusteringkurtosisfactor analysismixture modelsmatrix variate distributionskewed

Mathematics Subject Classification ID

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

Related Items (13)

On finite mixture modeling of change-point processes ⋮ MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling ⋮ Two new matrix-variate distributions with application in model-based clustering ⋮ Model-based clustering via skewed matrix-variate cluster-weighted models ⋮ Matrix variate generalized asymmetric Laplace distributions ⋮ A dual subspace parsimonious mixture of matrix normal distributions ⋮ Modelling students’ career indicators via mixtures of parsimonious matrix‐normal distributions ⋮ Parsimonious mixtures for the analysis of tensor-variate data ⋮ Visual assessment of matrix‐variate normality ⋮ CPclus: Candecomp/Parafac clustering model for three-way data ⋮ Least-squares bilinear clustering of three-way data ⋮ An evolutionary algorithm with crossover and mutation for model-based clustering ⋮ A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting

Uses Software

• Julia
• GitHub
• MatrixVariate.jl

Cites Work

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