Application of the Squared Mahalanobis Distance for Detecting Outliers in Multivariate Non-Gaussian Data

被引：0
|
作者：
Prykhodko, Sergiy [1 ]
Prykhodko, Natalia [2 ]
Makarova, Lidiia [1 ]
Pukhalevych, Andrii [1 ]
机构：
[1] Admiral Makarov Natl Univ Shipbldg, Dept Software Automated Syst, Mykolaiv, Ukraine
[2] Admiral Makarov Natl Univ Shipbldg, Dept Finance, Mykolaiv, Ukraine
来源：
2018 14TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET) | 2018年
关键词：
Mahalanobis distance; quantile; Chi-Square distribution; outlier detection; multiyariate non-Gaussian data; normalizing transfori nation;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Application of the squared Mahalanobis distance and a quantile of the Chi-Square distribution for detecting outliers in multivariate non-Gaussian data on the basis of univariate and multivariate normalizing transformations is considered. The examples of outlier detection in the four-dimensional non-Gaussian data are given.
引用
收藏
页码：962 / 965
页数：4
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