A general approach to categorical data analysis with missing data, using generalized linear models with composite links

DOI10.1007/BF02294657zbMath0850.62095OpenAlexW2004585031MaRDI QIDQ1205763

David Rindskopf

Publication date: 1 April 1993

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294657

Mathematics Subject Classification ID

Related Items (6)

Power and sample size computation for Wald tests in latent class models ⋮ Divisive latent class modeling as a density estimation method for categorical data ⋮ Multilevel and latent variable modeling with composite links and exploded likelihoods ⋮ Ill-posed problems with counts, the composite link model and penalized likelihood ⋮ Missing observations in paired comparison data ⋮ FAST CORRECTION ALGORITHMS FOR WEIGHTED EMPIRICAL DISTRIBUTION FUNCTIONS

Cites Work

• A General Approach to Analyzing Epidemiologic Data that Contain Misclassification Errors
• Product models for frequency tables involving indirect observation
• Log-linear models for frequency tables derived by indirect observation: Maximum likelihood equations
• Analysis of Categorical Data by Linear Models
• Composite Link Functions in Generalized Linear Models
• The Analysis of Contingency Tables with Incompletely Classified Data
• On the Use of Double Sampling Schemes in Analyzing Categorical Data with Misclassification Errors
• The Analysis of Partially Categorized Contingency Data
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