Expected Estimating Equations to Accommodate Covariate Measurement Error

DOI10.1111/1467-9868.00247zbMath0963.62065OpenAlexW2099811086MaRDI QIDQ4505998

C. Y. Wang, Margaret Sullivan Pepe

Publication date: 3 July 2001

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9868.00247

zbMATH Keywords

longitudinal datamaximum likelihoodlogistic regressionmissing datageneralized estimating equationregression calibration

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Generalized linear models (logistic models) (62J12)

Related Items (14)

APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data ⋮ Empirical likelihood inference for longitudinal data with covariate measurement errors: an application to the LEAN study ⋮ Optimal model averaging for semiparametric partially linear models with measurement errors ⋮ Inference in generalized linear regression models with a censored covariate ⋮ Heteroscedastic mixture transition distribution (HMTD) model ⋮ Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements ⋮ Functional methods for logistic regression on random-effect-coefficients for longitudinal measurements ⋮ Expected Estimating Equations for Missing Data, Measurement Error, and Misclassification, with Application to Longitudinal Nonignorable Missing Data ⋮ Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers ⋮ Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates ⋮ Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression ⋮ Imputation for statistical inference with coarse data ⋮ Partially Supervised Learning Using an EM‐Boosting Algorithm

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