MAHALANOBIS DISTANCE AND ITS APPLICATION FOR DETECTING MULTIVARIATE OUTLIERS

被引：151

作者：

Ghorbani, Hamid ^{[1
]}

机构：

[1] Univ Kashan, Dept Stat, Fac Math Sci, Kashan 8731753153, Iran

来源：

FACTA UNIVERSITATIS-SERIES MATHEMATICS AND INFORMATICS | 2019年 / 34卷 / 03期

关键词：

Mahalanobis distance; multivariate normal distribution; multivariate outliers; outlier detection; IDENTIFICATION;

D O I：

10.22190/FUMI1903583G

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

While methods of detecting outliers is frequently implemented by statisticians when analyzing univariate data, identifying outliers in multivariate data pose challenges that univariate data do not. In this paper, after short reviewing some tools for univariate outliers detection, the Mahalanobis distance, as a famous multivariate statistical distances, and its ability to detect multivariate outliers are discussed. As an application the univariate and multivariate outliers of a real data set has been detected using R software environment for statistical computing.

引用

页码：583 / 595

页数：13

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