Gaussian parsimonious clustering models with covariates and a noise component

DOI10.1007/s11634-019-00373-8zbMath1474.62240arXiv1711.05632OpenAlexW2973763519WikidataQ127226156 ScholiaQ127226156MaRDI QIDQ84391

Keefe Murphy, Thomas Brendan Murphy, Keefe Murphy

Publication date: 20 September 2019

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

Full work available at URL: https://arxiv.org/abs/1711.05632

zbMATH Keywords

EM algorithmmodel-based clusteringcovariatesparsimonymultivariate responsenoise componentmixtures of experts

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12)

Related Items

Conditional functional clustering for longitudinal data with heterogeneous nonlinear patterns, Multivariate cluster weighted models using skewed distributions, Semiparametric finite mixture of regression models with Bayesian P-splines, Merging components in linear Gaussian cluster-weighted models, A similarity-based Bayesian mixture-of-experts model, MoEClust

Uses Software

• mclust
• R
• flexmix
• mixtools
• Rmixmod
• flexCWM
• clustMD
• MoEClust
• PRMLT

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