THE IMPACT OF EXTREME OBSERVATIONS ON SIMPLE FORECASTING METHODS*
通过模拟研究比较最小绝对偏差（LAD）与最小二乘法（LS）在一阶自回归模型中对异常值的估计表现，发现LAD无法有效处理加性异常值，并提出一种简单的异常报告方法。
Abstract In general linear modeling, an alternative to the method of least squares (LS) is the least absolute deviations (LAD) procedure. Although LS is more widely used, the LAD approach yields better estimates in the presence of outliers. In this paper, we examine the performance of LAD estimators for the parameters of the first‐order autoregressive model in the presence of outliers. A simulation study compared these estimates with those given by LS. The general conclusion is that LAD does not deal successfully with additive outliers. A simple procedure is proposed which allows exception reporting when outliers occur.