Analysis of marginally specified semi‐nonparametric models for clustered binary data

DOI10.1111/j.1467-9574.2007.00355.xzbMath1121.62039OpenAlexW2026100050WikidataQ57188525 ScholiaQ57188525MaRDI QIDQ3592393

Santu Ghosh, Kalyan Das, Peter Congdon

Publication date: 13 September 2007

Published in: Statistica Neerlandica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9574.2007.00355.x

zbMATH Keywords

Metropolis-Hastings algorithmGLMMMCEM

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Generalized linear models (logistic models) (62J12)

Cites Work

• Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data
• Semi-Nonparametric Maximum Likelihood Estimation
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• The Identifiability of the Proportional Hazard Model
• The nonlinear mixed effects model with a smooth random effects density
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
• Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data
• Standard Errors for EM Estimates in Generalized Linear Models with Random Effects

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