Oracle, multiple robust and multipurpose calibration in a missing response problem

DOI10.1214/13-STS461zbMath1331.62070arXiv1410.3958MaRDI QIDQ252726

Sheung Chi Phillip Yam, Kwun Chuen Gary Chan

Publication date: 4 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.3958

zbMATH Keywords

robustnessmissing datageneralized empirical likelihoodmodel misspecification

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05)

Related Items (15)

Ensemble and calibration multiply robust estimation for quantile treatment effect ⋮ A fusion of least squares and empirical likelihood for regression models with a missing binary covariate ⋮ Empirical likelihood method for non-ignorable missing data problems ⋮ Jackknife empirical likelihood method for multiply robust estimation with missing data ⋮ General purpose multiply robust data integration procedures for handling nonprobability samples ⋮ Multiply robust imputation procedures for zero-inflated distributions in surveys ⋮ Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference ⋮ Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review ⋮ A unified framework of multiply robust estimation approaches for handling incomplete data ⋮ A unified empirical likelihood approach for testing MCAR and subsequent estimation ⋮ Empirical likelihood inference for non-randomized pretest-posttest studies with missing data ⋮ Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation ⋮ Robust estimation for moment condition models with data missing not at random ⋮ Multiply robust estimation in nonparametric regression with missing data ⋮ Oracle, multiple robust and multipurpose calibration in a missing response problem

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