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

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  • Guo, Donglin
  • Xue, Liugen
  • Hu, Yuqin

Abstract

Based on covariate balancing propensity score (CBPS), improved estimators for the regression coefficients and population mean of linear models are obtained, when the responses are missing at random. It is proved that the proposed estimators are asymptotically normal.

Suggested Citation

  • Guo, Donglin & Xue, Liugen & Hu, Yuqin, 2017. "Covariate-balancing-propensity-score-based inference for linear models with missing responses," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 139-145.
    • Handle: RePEc:eee:stapro:v:123:y:2017:i:c:p:139-145
    DOI: 10.1016/j.spl.2016.12.001
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
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    3. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
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    5. Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
    6. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    7. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    8. Kosuke Imai & Marc Ratkovic, 2015. "Robust Estimation of Inverse Probability Weights for Marginal Structural Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1013-1023, September.
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