| 基于多重共线性的Lasso方法 |
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| 引用本文： | 曹芳,朱永忠.基于多重共线性的Lasso方法[J].江南大学学报(自然科学版),2012,11(1):87-90. |
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| 作者姓名： | 曹芳 朱永忠 |
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| 作者单位： | 河海大学理学院,南京,210098 |
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| 基金项目： | 国家自然科学基金，河海大学自然科学基金 |
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| 摘 要： | 多重共线性是多元线性回归分析中的一个重要问题,消除共线性的危害一直是回归分析的一个重点.就此问题介绍了一种Lasso方法,并设计了一种选择最佳模型的方法.通过实例分析,将其与常用方法进行比较,从结果可看出,Lasso回归在处理多重共线性问题上较其他方法更加有效.
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| 关 键 词： | Lasso回归 主成分回归 岭回归 最小角回归算法 AIC准则 BIC准则 |
Based on Multi-linearity Lasso Method |
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| CAO Fang
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ZHU Yong-zhong.Based on Multi-linearity Lasso Method[J].Journal of Southern Yangtze University:Natural Science Edition,2012,11(1):87-90. |
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| Authors: | CAO Fang ZHU Yong-zhong |
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| Institution: | (College of Science,Hohai University,Nanjing 210098,China) |
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| Abstract: | High-dimensional multi-linearity has been a very important problem and how to eliminate the multi-linearity hazards regression analysis has been a priority.To address this problem we introduce more popular Lasso method and design a method of selecting best model.A real example is given to illustrate the calculation steps of the Lasso regression and it is compared with commonly used methods.From the results we can see that the Lasso regression is more effective in handling high-dimensional collinear problem when comparing with other methods. |
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| Keywords: | Lasso regression principal component regression ridge regression least angle regression algorithms AIC criterion BIC criterion |
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