基于正弦余弦算法的异常值线性回归  

Linear Regression of Outliers Based on Sine Cosine Algorithm

作　　者：雍龙泉[1,2] 贾伟 Yong Longquan;Jia Wei(School of Mathematics and Computer Science,Shaanxi University of Technology,Hanzhong Shaanxi 723001,China;Shaanxi Key Laboratory of Industrial Automation,Shaanxi University of Technology,Hanzhong Shaanxi 723001,China)

机构地区：[1]陕西理工大学数学与计算机科学学院,陕西汉中723001 [2]陕西理工大学陕西省工业自动化重点实验室,陕西汉中723001

出　　处：《统计与决策》2022年第6期37-39,共3页Statistics & Decision

基　　金：国家自然科学基金资助项目(11401357);陕西省教育厅重点科学研究计划项目(20JS021);陕省教育厅专项科研计划项目(17JK0146);陕西理工大学科研项目(SLGYQZX2002)。

摘　　要：对给定的数据,文章分别建立了以残差的平方和及绝对值和为目标的线性最小二乘与最小一乘模型,采用正弦余弦算法计算模型参数;然后应用于无异常值的模型和包含异常值的模型,计算结果发现异常值对最小二乘有着较大的影响,而对最小一乘的影响较小;表明最小一乘具有较好的稳健性。For the given data, this paper establishes linear least squares and least absolute deviation models respectively aiming at the sum of squares and sum of absolute values of residuals, calculating the parameters of models by sine cosine algorithm,and then applies it to models with and without outliers. The calculation results find that the outliers have a great effect on the least squares, but a little on the least absolute deviation, which indicates that the least absolute deviation is of good robustness.

关 键 词：最小二乘 最小一乘 正弦余弦算法 异常值 

分 类 号：O212.1[理学—概率论与数理统计]