Minimum Vector Variance Estimator in Outlier labeling of Multivariate Data: Application to HIV patient in Indonesia

Erna Tri Herdiani; Nurtiti Sunusi; Puji Puspa Sari

doi:10.6180/jase.202202_25(1).0002

Minimum Vector Variance Estimator in Outlier labeling of Multivariate Data: Application to HIV patient in Indonesia

Erna Tri Herdiani¹, Nurtiti Sunusi This email address is being protected from spambots. You need JavaScript enabled to view it.¹, and Puji Puspa Sari¹

¹Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, South Sulawesi, Indonesia, 90245

Received: October 7, 2020
Accepted: May 17, 2021
Publication Date: July 5, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202202_25(1).0002

ABSTRACT

An outlier is an observation whose pattern does not follow the majority of the data. Outliers in this study were characterized by extreme distance values, both very small and very large, exceeding the predetermined value. The method used in this research is Minimum Vector Variance (MVV) method because it has good computational efficiency and is robust against outliers. Based on the MVV algorithm applied to data on HIV patients in Indonesia in 2016-2018. The results showed that the MVV method produced more extreme distances than the Mahalanobis distance in labeling outliers. In the research data, it is found that there are 16 regions including outliers of the 34 observation used.

Keywords: HIV, MVV, Outliers, Mahalanobis Distance

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