Co-Clustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries

DOI10.1080/10618600.2020.1739533OpenAlexW3012326351MaRDI QIDQ5066747

Marco Corneli, Pierre Latouche, Charles Bouveyron

Publication date: 30 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2020.1739533

zbMATH Keywords

model based clusteringcategorical dataICLnot missing at random data

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Biclustering via structured regularized matrix decomposition ⋮ Co-clustering of evolving count matrices with the dynamic latent block model: application to pharmacovigilance ⋮ Co-clustering of multivariate functional data for the analysis of air pollution in the south of France ⋮ A survey on model-based co-clustering: high dimension and estimation challenges ⋮ Learning from missing data with the binary latent block model ⋮ Co-clustering of time-dependent data via the shape invariant model

Uses Software

• blockcluster
• clustMD
• PRMLT

Cites Work

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• Mixture models for ordinal data: a pairwise likelihood approach
• A Clustering Method for Categorical Ordinal Data
• Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood
• Estimation and selection for the latent block model on categorical data

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