ASYMPTOTICS FOR LEAST ABSOLUTE DEVIATION REGRESSION-ESTIMATORS
ASYMPTOTICS FOR LEAST ABSOLUTE DEVIATION REGRESSION-ESTIMATORS
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DOI：
10.1017/s0266466600004394
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发表时间：
1991-06-01
影响因子：
0.8
通讯作者：
POLLARD, D
中科院分区：
文献类型：
--
作者：
POLLARD, D
线性回归中向量参数的LAD估计是通过最小化残差的绝对值之和来定义的。本文给出了LAD估计量渐近正态性的一个直接证明。主要定理假设确定性载体。扩展到随机载体包括自回归的情况下，其误差项有有限的二阶矩。对于一阶Cauchy误差的自回归，LAD估计以1/n的速度收敛。
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.