论文标题
基于$ l_q $ -likelihoody for带有重尾错误的线性模型的正规化方法
Regularization Methods Based on the $L_q$-Likelihood for Linear Models with Heavy-Tailed Errors
论文作者
论文摘要
我们根据$ L_Q $ -likelihoodhie提出了线性模型的正则化方法，这是使用功率函数对日志可能性的概括。一些重尾分布称为$ Q $ - 正常分布。我们发现，具有$ q $ - 正常误差的线性模型的建议方法与应用于正常线性模型的正则化方法一致。所提出的方法可以很好地工作，并且可以使用现有软件包进行计算。我们使用数值实验检查了所提出的方法，即使误差是重尾，这些方法的性能也很好。
We propose regularization methods for linear models based on the $L_q$-likelihood, which is a generalization of the log-likelihood using a power function. Some heavy-tailed distributions are known as $q$-normal distributions. We find that the proposed methods for linear models with $q$-normal errors coincide with the regularization methods that are applied to the normal linear model. The proposed methods work well and efficiently, and can be computed using existing packages. We examine the proposed methods using numerical experiments, showing that the methods perform well, even when the error is heavy-tailed.
