Nonparametric Kernel Estimation for Semiparametric Models
证明了密度和回归函数及其导数的非参数核估计量的一致性，适用于依赖初步非参数估计的半参数估计和检验问题。
This paper presents a number of consistency results for nonparametric kernel estimators of density and regression functions and their derivatives. These results are particularly useful in semiparametric estimation and testing problems that rely on preliminary nonparametric estimators, as in Andrews (1994, Econometrica 62, 43–72). The results allow for near-epoch dependent, nonidentically distributed random variables, data-dependent bandwidth sequences, preliminary estimation of parameters (e.g., nonparametric regression based on residuals), and nonparametric regression on index functions.