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

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.