Bandwidth selection for nonparametric regression with errors-in-variables
针对自变量存在经典测量误差的非参数回归模型，提出了两种新的带宽选择方法，分别基于留一交叉验证和自助法，在蒙特卡洛模拟中比现有方法计算成本更低且均方积分误差更小。
We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.