Latent class analysis of incomplete data via an entropy-based criterion

DOI10.1016/j.stamet.2016.04.004zbMath1487.62071OpenAlexW2384791930WikidataQ31134352 ScholiaQ31134352MaRDI QIDQ670132

Ofer Harel, Katarzyna Kordas, Chantal Larose, Dey, Dipak K.

Publication date: 18 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5042216

zbMATH Keywords

entropymodel selectionmissing datamultiple imputationlatent class analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Uses Software

• UCI-ml
• poLCA
• COOLCAT

Cites Work

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• A Mathematical Theory of Communication
• A bootstrap-based aggregate classifier for model-based clustering
• An entropy criterion for assessing the number of clusters in a mixture model
• Finite mixture and Markov switching models.
• Latent class regression: Inference and estimation with two-stage multiple imputation
• Cluster Analysis
• Inference and missing data
• Model-Based Gaussian and Non-Gaussian Clustering
• Elements of Information Theory
• Average Entropy: A New Uncertainty Measure with Application to Image Segmentation
• A new look at the statistical model identification

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