Asymptotics for Least Absolute Deviation Regression Estimators
证明了线性回归中最小绝对偏差（LAD）估计量的渐近正态性，适用于确定性解释变量，并扩展到随机解释变量和自回归模型，包括误差项服从柯西分布的情形。
The LAD estimator of the vector parameter in a linear regression is defined by minimizing the sum of the absolute values of the residuals. This paper provides a direct proof of asymptotic normality for the LAD estimator. The main theorem assumes deterministic carriers. The extension to random carriers includes the case of autoregressions whose error terms have finite second moments. For a first-order autoregression with Cauchy errors the LAD estimator is shown to converge at a 1/ n rate.