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

机译:具有依赖误差的高阶数据锐化,以减少回归偏差

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摘要

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.
机译:在本文中,我们表明Y可以被引入数据锐化,以产生享受高偏差偏差的非参数回归估计。与现有文献中的那些相比,所提出的数据锐化估计器具有包括简单估计的优点,良好的期望和方差,以及温和的假设。我们将此估算者概括为依赖错误。最后,我们进行有限的模拟,以说明所提出的估计者比现有的估算更好。

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