Multivariate cluster weighted models using skewed distributions

DOI10.1007/s11634-021-00480-5OpenAlexW3211630849MaRDI QIDQ2673360

Paul D. McNicholas, Michael P. B. Gallaugher, Antonio Punzo, Salvatore D. Tomarchio

Publication date: 9 June 2022

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

Full work available at URL: https://arxiv.org/abs/2103.09792

zbMATH Keywords

clusteringskewed distributionsmixture modelscluster weighted models

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Related Items (2)

Model-based clustering via skewed matrix-variate cluster-weighted models ⋮ Merging components in linear Gaussian cluster-weighted models

Uses Software

• R
• sn
• QRM
• flexCWM
• MixGHD
• MoEClust

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Gaussian parsimonious clustering models with covariates and a noise component
• Mixtures of multivariate power exponential distributions
• Local statistical modeling via a cluster-weighted approach with elliptical distributions
• Decision boundaries for mixtures of regressions
• Clustering bivariate mixed-type data via the cluster-weighted model
• Analytic calculations for the EM algorithm for multivariate skew-\(t\) mixture models
• A maximum likelihood methodology for clusterwise linear regression
• Multivariate linear regression with non-normal errors: a solution based on mixture models
• Model-based clustering, classification, and discriminant analysis via mixtures of multivariate \(t\)-distributions
• Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results
• Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction
• Estimating the dimension of a model
• Identifiability of models for clusterwise linear regression
• Model-based clustering via linear cluster-weighted models
• Parsimonious skew mixture models for model-based clustering and classification
• Mixtures of skew-\(t\) factor analyzers
• Three skewed matrix variate distributions
• Clustering and classification via cluster-weighted factor analyzers
• Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions
• Matrix normal cluster-weighted models
• Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models
• A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models
• Subspace clustering for the finite mixture of generalized hyperbolic distributions
• The generalized linear mixed cluster-weighted model
• Multivariate response and parsimony for Gaussian cluster-weighted models
• Multilevel cluster-weighted models for the evaluation of hospitals
• Regularized multivariate regression models with skew-\(t\) error distributions
• Capturing patterns via parsimonious \(t\) mixture models
• Finite mixture and Markov switching models.
• Maximum likelihood estimation for multivariate skew normal mixture models
• Model-based clustering
• Turán type inequalities for some probability density functions
• Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions
• An Application of the Laplace Method to Finite Mixture Distributions
• A Mixture of Variance-Gamma Factor Analyzers
• Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling
• Model-Based Classification via Mixtures of Multivariatet-Factor Analyzers
• Flexible mixture modelling with the polynomial Gaussian cluster-weighted model
• The multivariate tail-inflated normal distribution and its application in finance
• Learning-based EM algorithm for normal-inverse Gaussian mixture model with application to extrasolar planets
• Inference and diagnostics in skew scale mixtures of normal regression models
• A mixture of generalized hyperbolic distributions
• Mixture Model-Based Classification
• Extending mixtures of multivariate \(t\)-factor analyzers

This page was built for publication: Multivariate cluster weighted models using skewed distributions