A nonlinear conditional probability model for generating correlated binary data

DOI10.1016/j.spl.2005.08.012zbMath1101.62074OpenAlexW2075981781MaRDI QIDQ2489881

Patrick J. Farrell, Brajendra Chandra Sutradhar

Publication date: 28 April 2006

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2005.08.012

zbMATH Keywords

Regression effectsKanter observation-driven dynamic modelNon-stationary correlationsRepeated binary responses

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Measures of association (correlation, canonical correlation, etc.) (62H20) Generalized linear models (logistic models) (62J12)

Related Items (8)

Methods for Generating Longitudinally Correlated Binary Data ⋮ Treatment design selection effects on parameter estimation in dynamic logistic models for longitudinal binary data ⋮ On probit versus logit dynamic mixed models for binary panel data ⋮ High-dimensional generation of Bernoulli random vectors ⋮ Inferences in generalized linear longitudinal mixed models ⋮ Inferences in dynamic logit models in semi-parametric setup for repeated binary data ⋮ A comparison of methods for simulating correlated binary variables with specified marginal means and correlations ⋮ Correlated endpoints: simulation, modeling, and extreme correlations

Cites Work

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• An overview on regression models for discrete longitudinal responses
• Autoregression for discrete processes mod 2
• A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations

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