Expected Estimating Equations for Missing Data, Measurement Error, and Misclassification, with Application to Longitudinal Nonignorable Missing Data

DOI10.1111/j.1541-0420.2007.00839.xzbMath1274.62893OpenAlexW2067109073WikidataQ31117584 ScholiaQ31117584MaRDI QIDQ5450458

Edward C. Chao, C. Y. Wang, Marjorie K. Jeffcoat, Yijian Huang

Publication date: 20 March 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00839.x

zbMATH Keywords

missing at randomestimating equationmisclassificationlatent modelnonignorable missingerror in variable

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (7)

Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error ⋮ Simultaneous mean and covariance estimation of partially linear models for longitudinal data with missing responses and covariate measurement error ⋮ Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables ⋮ Robust best linear estimation for regression analysis using surrogate and instrumental variables ⋮ Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates ⋮ Quantile regression in longitudinal studies with dropouts and measurement errors ⋮ Asymptotic theory for longitudinal data with missing responses adjusted by inverse probability weights

Cites Work

• Unnamed Item
• Estimation in choice-based sampling with measurement error and bootstrap analysis
• Measurement Error in the Response in the General Linear Model
• Multiple-Bias Modelling for Analysis of Observational Data
• Likelihood analysis for errors-in-variables regression with replicate measurements
• Modeling Repeated Measures With Monotonic Ordinal Responses and Misclassification, With Applications to Studying Maturation
• Consistent Functional Methods for Logistic Regression With Errors in Covariates
• Expected Estimating Equations to Accommodate Covariate Measurement Error
• Inference about Misclassification Probabilities from Repeated Binary Responses
• On robust estimation in logistic case-control studies
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Bias and efficiency loss due to misclassified responses in binary regression
• Measurement Error in Nonlinear Models
• Missing-Data Methods for Generalized Linear Models

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