A fusion of least squares and empirical likelihood for regression models with a missing binary covariate

DOI10.1007/s11425-016-5156-zzbMath1349.62042OpenAlexW2472300906MaRDI QIDQ341405

Zhi Wang, Xiao-gang Duan

Publication date: 16 November 2016

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-016-5156-z

zbMATH Keywords

calibrationmissing at randomeffect modificationcovariate adjustmentmultiple robustnessrefitting

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables

Uses Software

• SuperLearner

Cites Work

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• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
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• Empirical-Likelihood-Based Inference in Missing Response Problems and Its Application in Observational Studies
• Estimation with missing data: beyond double robustness
• Super Learner
• A Generalization of Sampling Without Replacement From a Finite Universe

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