Orthogonalized Residuals for Estimation of Marginally Specified Association Parameters in Multivariate Binary Data
DOI10.1111/j.1467-9469.2012.00802.xzbMath1323.62078OpenAlexW1776501425WikidataQ30578505 ScholiaQ30578505MaRDI QIDQ2914950
John S. Preisser, Bahjat F. Qaqish, Richard C. Zink
Publication date: 21 September 2012
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3501755
clustered datageneralized estimating equationsmarginal modelspairwise pseudo-likelihoodcorrelated binary observationsalternating logistic regressions
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20) Generalized linear models (logistic models) (62J12)
Related Items (4)
Cites Work
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- Longitudinal data analysis using generalized linear models
- Quasi-likelihood and its application. A general approach to optimal parameter estimation
- A pairwise likelihood approach to analyzing correlated binary data
- Models for Longitudinal Data: A Generalized Estimating Equation Approach
- Correlated Binary Regression with Covariates Specific to Each Binary Observation
- Permutation invariance of alternating logistic regression for multivariate binary data
- A Hybrid Pairwise Likelihood Method
- Efficiency of estimating equations and the use of pivots
- A note on the comparison of pseudo-likelihood and generalized estimating equations for marginally specified odds ratio models with exchangeable association structure
- Generalized binomial distributions
- Modelling multivariate binary data with alternating logistic regressions
- Estimating Equations for Measures of Association between Repeated Binary Responses
- Logistic Regression for Correlated Binary Data
- Range of correlation matrices for dependent Bernoulli random variables
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