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英文文献：Nonparametric Regression Under Alternative Data Environments-替代数据环境下的非参数回归
英文文献作者：Sam, Abdoul G.,Ker, Alan P.
英文文献摘要：
This paper proposes a nonparametric bias-reduction regression estimator which can accommodate two empirically relevant data environments. The first data environment assumes that at least one of the predictor variables is discrete. In such an empirical framework, a "cell" approach, which consists of estimating a separate regression for each discrete cell has generally been employed. However, the "cell" estimator may be inefficient in that it does not include data from the other cells when estimating the regression function for a given cell. The second data environment assumes that the researcher is faced with a system of regression functions that belong to different experimental units. In each case, the new estimator attempts to reduce estimation error by incorporating extraneous data from the remaining experimental units (or cells) when estimating a given individual regression function. Consistency of the proposed estimator is established and Monte Carlo simulations demonstrate its strong finite sample performance.
本文提出了一种能适应两种经验相关数据环境的非参数减偏回归估计量。第一个数据环境假设至少有一个预测变量是离散的。在这样一个经验框架中，“单元”的方法，包括估计一个单独的回归为每个离散单元通常被使用。然而，“单元”估计器可能是低效的，因为在估计一个给定单元的回归函数时，它不包括来自其他单元的数据。第二种数据环境假设研究者面对一个属于不同实验单位的回归函数系统。在每一种情况下，当估计一个给定的回归函数时，新的估计器试图通过合并来自剩余实验单元(或单元)的无关数据来减少估计误差。建立了估计量的一致性，并通过蒙特卡洛仿真验证了该估计量具有较强的有限样本性能。
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