Mixtures of skewed matrix variate bilinear factor analyzers
From MaRDI portal
Publication:2201326
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
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
Cites Work
- Unnamed Item
- Model-based classification using latent Gaussian mixture models
- Mixtures of multivariate power exponential distributions
- Analytic calculations for the EM algorithm for multivariate skew-\(t\) mixture models
- Finite mixtures of matrix normal distributions for classifying three-way data
- Model-based clustering, classification, and discriminant analysis via mixtures of multivariate \(t\)-distributions
- Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
- Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results
- Parsimonious skew mixture models for model-based clustering and classification
- Mixtures of skew-\(t\) factor analyzers
- On model-based clustering of skewed matrix data
- Three skewed matrix variate distributions
- A mixture of coalesced generalized hyperbolic distributions
- Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering
- Studying crime trends in the USA over the years 2000--2012
- Capturing patterns via parsimonious \(t\) mixture models
- Model based clustering for three-way data structures
- Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions
- Julia: A Fresh Approach to Numerical Computing
- On matrix variate skew-normal distributions
- A Mixture of Variance-Gamma Factor Analyzers
- A mixture of generalized hyperbolic distributions
- Matrix variate skew normal distributions
- A Matrix Variate Closed Skew-Normal Distribution with Applications to Stochastic Frontier Analysis
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- Extending mixtures of multivariate \(t\)-factor analyzers
This page was built for publication: Mixtures of skewed matrix variate bilinear factor analyzers
