@article{luo_zhang_xu_2014, 
  title={The Local LinearM-Estimation with Missing Response Data}, 
  volume={2014}, 
  DOI={10.1155/2014/398082}, 
  abstractNote={<jats:p>This paper studies the nonparametric regressive function with missing response data. Three local linear<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-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<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-estimator is not superior to the other two local linear<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:math>-estimators.</jats:p>}, 
  publisher={Hindawi Limited}, 
  author={Luo and Zhang and Xu}, 
  year={2014}
  }