Evaluating Robust Regression Techniques for Detrending Crop Yield Data with Nonnormal Errors
比较了普通最小二乘法与六种稳健回归方法在作物产量趋势估计中的表现，发现前者在非正态误差下更有效，并推荐使用DFBETAS诊断统计量检测序列末尾的异常值。
Abstract Although ordinary least squares is not efficient when errors are not distributed normally, it generates better crop yield trend coefficient estimates than six alternative robust regression methods. This is because of the econometric properties of an uninterrupted series independent variable as well as the level of skewness typical of corn yields. The evaluation covers actual farm‐level corn yield series as well as a set of “contaminated” data series and one thousand sets of Monte Carlo yield series. Where an influential end‐of‐series outlier is suspected, the DFBETAS regression diagnostic statistic is recommended.