Hierarchical likelihood methods for nonlinear and generalized linear mixed models with missing data and measurement errors in covariates

DOI10.1016/J.JMVA.2012.02.011zbMath1241.62103OpenAlexW2158487021MaRDI QIDQ432295

Maengseok Noh, Lang Wu, Youngjo Lee

Publication date: 4 July 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2012.02.011

zbMATH Keywords

missing covariatesnonlinear mixed effects models

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) General nonlinear regression (62J02)

Related Items (4)

A pairwise likelihood approach for longitudinal data with missing observations in both response and covariates ⋮ Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates ⋮ Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm ⋮ Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies

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