Empirical likelihood inference for non-randomized pretest-posttest studies with missing data

DOI10.1214/19-EJS1566zbMath1420.62209OpenAlexW2952566994MaRDI QIDQ2002580

Changbao Wu, Shixiao Zhang, Peisong Han

Publication date: 12 July 2019

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1561168837

zbMATH Keywords

missing at randomempirical likelihoodauxiliary informationtreatment effectbiased samplingobservational studymultiple robustness

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Nonparametric robustness (62G35) Nonparametric tolerance and confidence regions (62G15)

Related Items (6)

Two-step calibration estimation under multiple auxiliary variables in survey sampling ⋮ Restricted survival benefit with right‐censored data ⋮ Multiply robust estimation of the average treatment effect with missing outcomes ⋮ Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference ⋮ A multiply robust Mann-Whitney test for non-randomised pretest-posttest studies with missing data ⋮ Comments on: ``Deville and Särndal's calibration: revisiting a 25 years old successful optimization problem

Cites Work

• Unnamed Item
• Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation
• Oracle, multiple robust and multipurpose calibration in a missing response problem
• Empirical likelihood and general estimating equations
• Empirical likelihood inference for two-sample problems
• A further study of the multiply robust estimator in missing data analysis
• Semiparametric theory and missing data.
• Semiparametric estimation of treatment effect in a pretest-posttest study with missing data (with comments and rejoinder)
• Mann–Whitney test with empirical likelihood methods for pretest–posttest studies
• An imputation based empirical likelihood approach to pretest-posttest studies
• Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study
• Semiparametric Estimation of Treatment Effect in a Pretest‐Posttest Study
• The central role of the propensity score in observational studies for causal effects
• Inference and missing data
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Regression Models for the Analysis of Pretest/Posttest Data
• Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys
• Multiply Robust Estimation in Regression Analysis With Missing Data
• Empirical-Likelihood-Based Inference in Missing Response Problems and Its Application in Observational Studies
• Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial
• Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.
• OUP accepted manuscript
• Estimation with missing data: beyond double robustness

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