Identifiability of extended latent class models with individual covariates

DOI10.1016/j.csda.2008.04.030zbMath1452.62059OpenAlexW1963776537MaRDI QIDQ1023901

Antonio Forcina

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.04.030

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Generalized linear models (logistic models) (62J12) Contingency tables (62H17)

Related Items (9)

Power and sample size computation for Wald tests in latent class models ⋮ Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests ⋮ A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models ⋮ Likelihood-based tests for a class of misspecified finite mixture models for ordinal categorical data ⋮ Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis ⋮ A hybrid symbolic-numerical method for determining model structure ⋮ Parameter redundancy in capture-recapture-recovery models ⋮ Parameter redundancy in discrete state-space and integrated models ⋮ Empirical definition of social types in the analysis of inequality of opportunity: a latent classes approach

Uses Software

• Latent GOLD

Cites Work

• Maximum likelihood methods for a generalized class of log-linear models
• Marginal models for categorical data
• Building an identifiable latent class model with covariate effects on underlying and measured variables
• Likelihood inference on the underlying structure of IRT models
• Testing for local dependency in dichotomous and polytomous item response models
• Latent Variable Modeling of Diagnostic Accuracy
• Detecting parameter redundancy
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• A Class of Latent Marginal Models for Capture–Recapture Data With Continuous Covariates
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