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The Local Linear M‐Estimation with Missing Response Data

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  • Shuanghua Luo
  • Cheng-Yi Zhang
  • Fengmin Xu

Abstract

This paper studies the nonparametric regressive function with missing response data. Three local linear M‐estimators with the robustness of local linear regression smoothers are presented such that they have the same asymptotic normality and consistency. Then finite‐sample performance is examined via simulation studies. Simulations demonstrate that the complete‐case data M‐estimator is not superior to the other two local linear M‐estimators.

Suggested Citation

  • Shuanghua Luo & Cheng-Yi Zhang & Fengmin Xu, 2014. "The Local Linear M‐Estimation with Missing Response Data," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:398082
    DOI: 10.1155/2014/398082
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    References listed on IDEAS

    as
    1. Wang, Qihua & Sun, Zhihua, 2007. "Estimation in partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1470-1493, August.
    2. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
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