Mann–Whitney test with empirical likelihood methods for pretest–posttest studies

DOI10.1080/10485252.2016.1163354zbMath1338.62128OpenAlexW2314978384MaRDI QIDQ2811284

Mary E. Thompson, Min Chen, Changbao Wu

Publication date: 10 June 2016

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

Full work available at URL: https://doi.org/10.1080/10485252.2016.1163354

zbMATH Keywords

bootstrapjackknife empirical likelihoodempirical likelihood ratio testbaseline informationimputation for missing datamissing by design

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items (3)

Hypothesis testing for two population means: parametric or non-parametric test? ⋮ 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

Cites Work

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• Bayesian inference for causal effects: The role of randomization
• Semiparametric estimation of treatment effect in a pretest-posttest study with missing data (with comments and rejoinder)
• 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
• Information Recovery in a Study With Surrogate Endpoints
• Efficiency Study of Estimators for a Treatment Effect in a Pretest–Posttest Trial
• Jackknife Empirical Likelihood
• Empirical likelihood inference for a common mean in the presence of heteroscedasticity
• On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other

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