Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form
提出在多元回归模型中，当残差方差是解释变量的未知函数时，用最近邻非参数估计估计方差，得到的加权最小二乘估计量具有与已知方差函数或有限参数化估计量相同的渐近分布。
In a multiple regression model the residual variance is an unknown function of the explanatory variables, and estimated by nearest neighbor nonparametric regression. The resulting weighted least squares estimator of the regression coefficients is shown to be adaptive, in the sense of having the same asymptotic distribution, to first order, as estimators based on knowledge of the actual variance function or a finite parameterization of it. A similar result was established by Carroll (1982) using kernel estimation and under substantially more restrictive conditions on the data generating process than ours. Extensions to various other models seem to be possible.