基于Robust回归的湖北省粮食产量预测  

Prediction of Grain Output in Hubei Province Based on Robust Regression

作　　者：宋良美 SONG Liangmei(China University of Geosciences(Wuhan),Wuhan Hubei 430074)

机构地区：[1]中国地质大学(武汉)经济管理学院,湖北武汉430074

出　　处：《陕西国防职教研究》2025年第1期24-27,共4页Shaanxi Guofang Vocational Education Research

摘　　要：Robust回归是一种用于处理异常值或极端数据的回归方法,它通过对偏离数据加权重,对经典最小二乘回归中的目标函数进行修改,减小数据中的异常值或噪声干扰,使其对模型参数估计的影响减小。由于粮食产量受自然气候条件影响较大,容易出现异常情况。因此,运用Robust回归模型对湖北省粮食产量进行预测。结果显示:平均预测误差仅为0.93020%,比OLS回归的减小了5.9568%。Robust Regression is a regression method used to handle outliers or extreme data.By adding weight to the deviation data,it changes the objective function in the classical least squares regression to reduce the outliers or noise interference in the data,so as to reduce its impact on the model parameter estimation.Because the grain yield is greatly affected by the natural climate conditions,it is easy to appear abnormal conditions.Therefore,the Robust regression model was used to predict the grain yield in Hubei Province.The results show that the average prediction error is only 0.93020%,which is 5.9568%less than the OLS regression.

关 键 词：粮食产量 预测 播种面积 Robust回归 

分 类 号：F224.7[经济管理—国民经济]