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

Shuanghua Luo () and Cheng-yi Zhang ()
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Shuanghua Luo: Xi’an Jiaotong University
Cheng-yi Zhang: Xi’an Polytechnic University

Statistical Papers, 2016, vol. 57, issue 3, No 5, 664 pages

Abstract: 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.

Keywords: Nonparametric $$M$$ M -type regression; Robust nonparametric $$M$$ M -estimator; The complete-case $$M$$ M -estimator; The weighted $$M$$ M -estimator; The estimated weighted $$M$$ M -estimator; The imputed $$M$$ M -estimator; 62G05; 62G15; 62J05 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-015-0672-4

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