Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis

DOI10.1007/s11634-015-0211-0zbMath1414.62263OpenAlexW789391616WikidataQ57569386 ScholiaQ57569386MaRDI QIDQ2418264

Daniel L. Oberski

Publication date: 3 June 2019

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

Full work available at URL: https://doi.org/10.1007/s11634-015-0211-0

zbMATH Keywords

score testinformation criterialatent class analysislocal dependencebivariate residualvote misclassification

Mathematics Subject Classification ID

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

Related Items (2)

Statistical inference in constrained latent class models for multinomial data based on \(\phi\)-divergence measures ⋮ Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models

Uses Software

• ElemStatLearn
• Latent GOLD

Cites Work

• A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models
• Identifiability of extended latent class models with individual covariates
• Building an identifiable latent class model with covariate effects on underlying and measured variables
• Enhancing the selection of a model-based clustering with external categorical variables
• Local dependence latent structure models
• Random Effects Models in Latent Class Analysis for Evaluating Accuracy of Diagnostic Tests
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach
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