Goal Programming Approach for Regression Median*
提出一种新的稳健估计方法，通过两个目标规划模型得到回归中位数估计量，相比最小绝对偏差估计对样本量小和误差偏态分布更不敏感，并用数据和模拟验证了优越性。
ABSTRACT This study presents a new robust estimation method that can produce a regression median hyper‐plane for any data set. The robust method starts with dual variables obtained by least absolute value estimation. It then utilizes two specially designed goal programming models to obtain regression median estimators that are less sensitive to a small sample size and a skewed error distribution than least absolute value estimators. The superiority of new robust estimators over least absolute value estimators is confirmed by two illustrative data sets and a Monte Carlo simulation study.