L-Estimation for Linear Models
本文提出了线性回归模型参数的L估计方法，基于回归分位数的线性组合，并建立了回归分位数的均匀Bahadur表示，为包括光滑权重函数的L估计提供了理论基础。一个主要例子是修剪均值的类似物，相比早期提议有优势。
Analogues of linear-combinations-of-order-statistics, or L-estimators, are suggested for estimating the parameters of the linear regression model.The methods are based on linear combinations of the p-dimensional "regression quantiles" proposed by Koenker and Bassett.A uniform Bahadur-type representation of regression quantiles is established, and this permits a general theory of L-estimators based on regression quantiles including those with smooth weight functions.A leading example of the proposed class of estimators is an analogue of the trimmed mean which seems to exhibit certain advantages over earlier proposals by Koenker and Bassett and Ruppert and Carroll.A brief investigation of two proposals for estimating the covariance matrix of this estimator is also reported.