Least Absolute Value Regression: Necessary Sample Sizes to Use Normal Theory Inference Procedures*
通过蒙特卡洛模拟，研究了最小绝对值回归中正态理论推断程序有效所需的样本量，发现样本量需求从20（正态扰动）到200（极端异常值分布）不等。
ABSTRACT Recently developed large sample inference procedures for least absolute value (LAV) regression are examined via Monte Carlo simulation to determine when sample sizes are large enough for the procedures to work effectively. A variety of different experimental settings were created by varying the disturbance distribution, the number of explanatory variables and the way the explanatory variables were generated. Necessary sample sizes range from as small as 20 when disturbances are normal to as large as 200 in extreme outlier‐producing distributions.