Robust inference for parsimonious model-based clustering

DOI10.1080/00949655.2018.1554659OpenAlexW2902525526WikidataQ128821737 ScholiaQ128821737MaRDI QIDQ5107332

Francesco Dotto, Alessio Farcomeni

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11573/1219616

zbMATH Keywords

mclusttclustgroupsfactor modelaffine invariance

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Robust fitting of mixtures of GLMs by weighted likelihood ⋮ Robust model-based clustering with mild and gross outliers ⋮ A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise ⋮ Unconstrained representation of orthogonal matrices with application to common principal components ⋮ Weighted likelihood mixture modeling and model-based clustering ⋮ mtclust

Uses Software

• mclust
• teigen
• TCLUST
• otrimle
• ContaminatedMixt
• GitHub

Cites Work

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• Robust Methods for Data Reduction
• Avoiding spurious local maximizers in mixture modeling

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