An Alternative Method of Detecting Outlier in Multivariate Data using Covariance Matrix

An Alternative Method of Detecting Outlier in Multivariate Data using Covariance Matrix

Obafemi, O.S. * §

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Alabi, N.O.

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An Alternative Method of Detecting Outlier in Multivariate Data using Covariance Matrix

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Abstract

In the Multivariate data analysis, the detection of outliers is important and necessary though this may be difficult and can pose a problem to the analyst. When a set of data is contaminated, the values obtained from such set of data are distorted and the results meaningless. In this work we present a simple multivariate outlier detection procedure using a robust estimator for variance-covariance matrix by using the best units from the available data set that satisfied the three predetermined optimality criteria, selected from all possible combinations of sub-sample obtained. The proposed estimator used is the variance-covariance estimator of the best unit multiplied by a constant. It is observed that, the proposed method combined the efficiencies of the classical and the existing robust (MCD and MVE) of being able to signal when there are few and multiple outliers in multivariate data.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Obafemi, O.S., Alabi, N.O.. 2019. "An Alternative Method of Detecting Outlier in Multivariate Data using Covariance Matrix". Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 19 (GJSFR Volume 19 Issue F4).

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GJSFR Volume 19 Issue F4

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

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Classification

GJSFR-F Classification MSC 2010: 97K80

Submission ReceivedJanuary 1, 1970
Peer Review Double Blind
Handling Editor
Accepted January 1, 1970

Version of record

v1.2

Issue date

November 25, 2019

Language

English

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Article Matrices

Total Score: 147

Country: Nigeria

Subject: Global Journal of Science Frontier Research

Authors: Obafemi O.S. , Alabi, N.O. (PhD/Dr. count: 0)

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Publish Date: 2019 08, Fri

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