基于水平镜像算法的改进Box-Cox变换

Improved Box Cox Transform Based on Horizontal Mirror Algorithm

摘要: 基于服从负偏态分布数据的一种水平镜像算法，本文提出一种改进Box-Cox变换——镜像Box-Cox变换，并进行数值实验，实验结果显示，与传统的Box-Cox变换相比较，镜像Box-Cox变换在处理正偏态分布数据的效果上与传统Box-Cox变换处理效果相同的基础上，其处理负偏态分布数据的效果要优于传统Box-Cox变换。再进行模拟回归模型实验，实验结果表明，经过镜像Box-Cox变换的数据建立的回归模型的拟合和预测效果有所提高，且效果优于使用传统Box-Cox变换后的数据。

Abstract: Based on a horizontal mirror algorithm for data with negative skew distribution, this paper proposes an improved Box-Cox transform: mirror Box-Cox transform, and carries out numerical experiments. The experimental results show that, compared with the traditional Box-Cox transform, mirror Box-Cox transform can process negative skewness on the basis of the same effect as the traditional Box-Cox transform. The effect of distributed data is better than that of traditional Box-Cox transform. Then the simulated regression model experiment is carried out. The experimental results show that the fitting and prediction effect of the regression model established by the mirror Box-Cox transformation data is improved, and the effect is better than the data after using the traditional Box-Cox transformation.