A Review on Outliers-Detection Methods for Multivariate Data

Authors

  • Sharifah Sakinah Syed Abd Mutalib Centre for Mathematical Sciences, College of Computing & Applied Sciences, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang
  • Siti Zanariah Satari Centre for Mathematical Sciences, College of Computing & Applied Sciences, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang
  • Wan Nur Syahidah Wan Yusoff Centre for Mathematical Sciences, College of Computing & Applied Sciences, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang

DOI:

https://doi.org/10.22452/josma.vol3no1.1

Keywords:

Outliers, Multivariate data, Robust estimator, Mahalanobis distance, Projection pursuit

Abstract

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.

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Published

2021-06-29

How to Cite

Syed Abd Mutalib, S. S. ., Satari, S. Z., & Wan Yusoff, W. N. S. (2021). A Review on Outliers-Detection Methods for Multivariate Data. Journal of Statistical Modeling &Amp; Analytics (JOSMA), 3(1). https://doi.org/10.22452/josma.vol3no1.1

Issue

Vol. 3 No. 1 (2021): Journal of Statistical Modeling & Analytics (JOSMA)

Section

Articles