A Shortcut to LAD Estimator Asymptotics
利用随机变量的广义函数和广义泰勒级数展开，快速证明了回归模型中LAD估计量的渐近理论，并得到了分布渐近展开等新结果。
Using generalized functions of random variables and generalized Taylor series expansions, we provide quick demonstrations of the asymptotic theory for the LAD estimator in a regression model setting. The approach is justified by the smoothing that is delivered in the limit by the asymptotics, whereby the generalized functions are forced to appear as linear functionals wherein they become real valued. Models with fixed and random regressors, and autoregressions with infinite variance errors are studied. Some new analytic results are obtained including an asymptotic expansion of the distribution of the LAD estimator.