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Nonparametric $$M$$ M -type regression estimation under missing response data

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  • Shuanghua Luo

    (Xi’an Jiaotong University
    Xi’an Polytechnic University)

  • Cheng-yi Zhang

    (Xi’an Polytechnic University)

Abstract

This paper studies the nonparametric $$M$$ M -type regressive function with missing response data. Three robust nonparametric $$M$$ M -estimators including the complete-case $$M$$ M -estimator, the weighted $$M$$ M -estimator and the imputed $$M$$ M -estimator are proposed firstly, and then their asymptotic normality and consistency are well proved. Finally, finite-sample performance is examined via simulation studies. Simulations demonstrate that the imputed $$M$$ M -estimator is superior to the other two local linear M-estimators.

Suggested Citation

  • Shuanghua Luo & Cheng-yi Zhang, 2016. "Nonparametric $$M$$ M -type regression estimation under missing response data," Statistical Papers, Springer, vol. 57(3), pages 641-664, September.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:3:d:10.1007_s00362-015-0672-4
    DOI: 10.1007/s00362-015-0672-4
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    References listed on IDEAS

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    Cited by:

    1. Yu-Ye Zou & Han-Ying Liang, 2020. "CLT for integrated square error of density estimators with censoring indicators missing at random," Statistical Papers, Springer, vol. 61(6), pages 2685-2714, December.
    2. Tianqing Liu & Xiaohui Yuan, 2020. "Empirical likelihood-based weighted rank regression with missing covariates," Statistical Papers, Springer, vol. 61(2), pages 697-725, April.

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