REVISITING THE EVALUATION OF ROBUST REGRESSION TECHNIQUES FOR CROP YIELD DATA DETRENDING
REVISITING THE EVALUATION OF ROBUST REGRESSION TECHNIQUES FOR CROP YIELD DATA DETRENDING
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DOI：
10.1093/ajae/aap021
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发表时间：
2010-01-01
影响因子：
4.2
通讯作者：
Finger, Robert
中科院分区：
文献类型：
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作者：
Finger, Robert
通过蒙特卡罗实验，比较了普通最小二乘（OLS）和mm估计（一种鲁棒回归技术）在作物产量去趋势分析中的应用。假设作物产量分布对称和偏斜，我们表明mm估计器在未污染的作物产量数据时间序列上的表现与OLS相似，并且明显优于OLS在异常值污染样本上的表现。与早期的研究相比，我们的分析表明，应该重新考虑稳健回归技术，如mm估计器，以消除作物产量数据的趋势。
Using a Monte Carlo experiment, the performance of the ordinary least squares (OLS) and the MM-estimator, a robust regression technique, is compared in an application of crop yield detrending. Assuming symmetric as well as skewed crop yield distributions, we show that the MM-estimator performs similarly to OLS for uncontaminated time series of crop yield data, and clearly outperforms OLS for outlier-contaminated samples. In contrast to earlier studies, our analysis suggests that robust regression techniques, such as the MM-estimator, should be reconsidered for detrending crop yield data.