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High-order data sharpening with dependent errors for regression bias reduction

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  • Xuyang He
  • Yuexiang Jiang
  • Jiazhen Wang

Abstract

In this paper, we show that Y can be introduced into data sharpening to produce non-parametric regression estimators that enjoy high orders of bias reduction. Compared with those in existing literature, the proposed data-sharpening estimator has advantages including simplicity of the estimators, good performance of expectation and variance, and mild assumptions. We generalize this estimator to dependent errors. Finally, we conduct a limited simulation to illustrate that the proposed estimator performs better than existing ones.

Suggested Citation

  • Xuyang He & Yuexiang Jiang & Jiazhen Wang, 2019. "High-order data sharpening with dependent errors for regression bias reduction," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(23), pages 5748-5755, December.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:23:p:5748-5755
    DOI: 10.1080/03610926.2018.1520885
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