An imputation based empirical likelihood approach to pretest-posttest studies

DOI10.1002/cjs.11254zbMath1321.62037OpenAlexW1556389145MaRDI QIDQ2949766

Min Chen, Mary E. Thompson, Changbao Wu

Publication date: 2 October 2015

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

Full work available at URL: https://doi.org/10.1002/cjs.11254

zbMATH Keywords

kernel estimationtreatment effectregression imputationbaseline informationmissing by design

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (3)

A multiply robust Mann-Whitney test for non-randomised pretest-posttest studies with missing data ⋮ Empirical likelihood inference for non-randomized pretest-posttest studies with missing data ⋮ Mann–Whitney test with empirical likelihood methods for pretest–posttest studies

Cites Work

• Empirical likelihood inference for two-sample problems
• Empirical likelihood-based inference under imputation for missing response data
• Semiparametric estimation of treatment effect in a pretest-posttest study with missing data (with comments and rejoinder)
• Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study
• Analysis of Covariance in Parallel-Group Clinical Trials with Pretreatment Baselines
• Semiparametric Estimation of Treatment Effect in a Pretest‐Posttest Study
• Semiparametric dimension reduction estimation for mean response with missing data
• Nonparametric Estimation of Mean Functionals with Data Missing at Random
• Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial
• The elements of statistical learning. Data mining, inference, and prediction

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