Kernel Averaging Estimators
提出一种模型平均方法，通过对不同带宽下的Nadaraya-Watson核估计量进行加权平均，并最小化最小二乘交叉验证准则来选择权重，从而避免带宽选择的不确定性，并证明该组合估计量能达到渐近最小均方误差。
The issue of bandwidth selection is a fundamental model selection problem stemming from the uncertainty about the smoothness of the regression. In this article, we advocate a model averaging approach to circumvent the problem caused by this uncertainty. Our new approach involves averaging across a series of Nadaraya-Watson kernel estimators each under a different bandwidth, with weights for these different estimators chosen such that a least-squares cross-validation criterion is minimized. We prove that the resultant combined-kernel estimator achieves the smallest possible asymptotic aggregate squared error. The superiority of the new estimator over estimators based on widely accepted conventional bandwidth choices in finite samples is demonstrated in a simulation study and a real data example.