A unified empirical likelihood approach for testing MCAR and subsequent estimation

DOI10.1111/sjos.12351zbMath1417.62025OpenAlexW2887069295MaRDI QIDQ4629283

Peisong Han, Changbao Wu, Shixiao Zhang

Publication date: 21 March 2019

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2027.42/147870

zbMATH Keywords

calibrationempirical likelihoodsurvey samplingmissing completely at randomregression modelmissingness mechanism

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric estimation (62G05) Sampling theory, sample surveys (62D05)

Related Items (4)

Two-step calibration estimation under multiple auxiliary variables in survey sampling ⋮ Score Test for Missing at Random or Not under Logistic Missingness Models ⋮ Testing the missing at random assumption in generalized linear models in the presence of instrumental variables ⋮ Comments on: ``Deville and Särndal's calibration: revisiting a 25 years old successful optimization problem

Cites Work

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• Longitudinal data analysis using generalized linear models
• Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation
• Oracle, multiple robust and multipurpose calibration in a missing response problem
• Tests of homogeneity of means and covariance matrices for multivariate incomplete data
• Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data
• A nonparametric test of missing completely at random for incomplete multivariate data
• Empirical likelihood and general estimating equations
• Bounded, efficient and doubly robust estimation with inverse weighting
• Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses
• Empirical likelihood ratio confidence intervals for a single functional
• Calibration Estimators in Survey Sampling
• A test of missing completely at random for generalised estimating equations with missing data
• A Test of the Missing Data Mechanism for Repeated Categorical Data
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data
• Multiply Robust Estimation in Regression Analysis With Missing Data
• Empirical-Likelihood-Based Inference in Missing Response Problems and Its Application in Observational Studies
• Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.
• Estimation with missing data: beyond double robustness
• Computing the distribution of quadratic forms in normal variables
• A Generalization of Sampling Without Replacement From a Finite Universe

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