A review on outliers-detection methods for multivariate data

• Main Authors: Sharifah Sakinah, Syed Abd Mutalib, Siti Zanariah, Satari, Wan Nur Syahidah, Wan Yusoff
• Format: Article
• Language: English; English
• Published: Institute of Statistics Malaysia (ISMy) 2021
• Subjects: QA Mathematics
• Online Access: http://umpir.ump.edu.my/id/eprint/31672/1/30586; http://umpir.ump.edu.my/id/eprint/31672/2/2021%20Abd%20Mutalib%20et%20al%20JOSMA.pdf; http://umpir.ump.edu.my/id/eprint/31672/; https://doi.org/10.22452/josma.vol3no1.1
• Institution: Universiti Malaysia Pahang

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed.