Optimal Kernel Weights Under a Power Criterion
提出一种以最大化检验功效而非最小化均方误差为准则的非参数回归斜率估计的最优核权重选择方法，推导更简单且直观。
Abstract We develop an approach to choosing optimal kernel weights for nonparametric estimation of the slope of the regression function which uses maximization of power, rather than minimization of integrated mean squared error (IMSE), as its optimality criterion. This power criterion leads to optimal kernel weights whose derivation is simpler than under other criteria and which provides an intuitive understanding of the nature of the optimality.