Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations.

DOI10.1016/S0167-9473(01)00029-9zbMath1095.62512OpenAlexW2137458905WikidataQ126758667 ScholiaQ126758667MaRDI QIDQ5958227

Michel Meulders, Paul De Boeck, Iven van Mechelen

Publication date: 3 March 2002

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

Full work available at URL: https://doi.org/10.1016/s0167-9473(01)00029-9

zbMATH Keywords

Discrete dataGeneralized linear modelLink functionMain-effects modelMatrix decomposition

Mathematics Subject Classification ID

Point estimation (62F10) Generalized linear models (logistic models) (62J12)

Related Items (4)

Constrained multilevel latent class models for the analysis of three-way three-mode binary data ⋮ APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ A taxonomy of latent structure assumptions for probability matrix decomposition models ⋮ Probabilistic Feature Analysis of Facial Perception of Emotions

Uses Software

• R
• S-PLUS
• BayesDA
• PROC GENMOD
• SAS/STAT

Cites Work

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• Probability matrix decomposition models
• The conjunctive model of hierarchical classes
• Psychometric latent response models
• Bayesianly justifiable and relevant frequency calculations for the applied statistician
• Posterior predictive \(p\)-values
• INDCLAS: a three-way hierarchical classes model
• The Calculation of Posterior Distributions by Data Augmentation
• On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models
• Diagnostic Checks for Discrete Data Regression Models Using Posterior Predictive Simulations
• Some Methods for Strengthening the Common χ 2 Tests

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