Covariate-balancing-propensity-score-based inference for linear models with missing responses

DOI10.1016/j.spl.2016.12.001zbMath1463.62203OpenAlexW2561153764MaRDI QIDQ511569

Donglin Guo, Yuqin Hu, Liu Gen Xue

Publication date: 21 February 2017

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2016.12.001

zbMATH Keywords

linear modelmissing at randomGMMrobust estimationaugmented inverse probability weightedcovariate balancing propensity score

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Missing data (62D10)

Related Items (1)

Model averaging procedure for varying-coefficient partially linear models with missing responses

Cites Work

• Large Sample Properties of Generalized Method of Moments Estimators
• Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
• On empirical likelihood for linear models with missing responses
• Empirical likelihood for linear models with missing responses
• Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
• A Distributional Approach for Causal Inference Using Propensity Scores
• 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
• Empirical Likelihood-based Inference in Linear Models with Missing Data
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Robust Estimation of Inverse Probability Weights for Marginal Structural Models
• Covariate Balancing Propensity Score
• A Generalization of Sampling Without Replacement From a Finite Universe

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