基于均值漂移模型的异常值检测方法

Outlier Detection Method Based on the Mean Shift Model

由于异常值的存在对统计推断有很大影响,因此异常值检测是数据分析中的一个重要步骤。对于横截面数据的线性模型,改写模型的设计矩阵后,基于均值漂移模型,利用系数压缩估计方法来进行异常值检测。由于系数压缩估计中调节参数的选择对检测效果有很大影响,基于两种调节方法的加权,提出了一种新的调节方法。数值模拟结果表明,使用这种基于均值漂移模型的异常值检测调节方法,可以显著降低犯两种错误的概率。

Outlier detection is an important part for data analysis, since outliers can infect the statistical inference evidently. The design matrix in a linear model for cross sectional data was rewritten, and an outlier detection method was proposed based on the mean shift model by using the coefficient shrink estimation. Because the selection of tuning model parameters is very important for outlier detection, a new tuning method based on a weighted tuning process was proposed. The numerical simulation results show that when the new tuning method is applied in the outlier detection procedure, two false identification probability can be decreased observably.