A Robust Method of Estimating Covariance Matrix in Multivariate Data Analysis
| dc.contributor.author | Oyeyemi, G. M. | |
| dc.contributor.author | Ipinyomi, R. A. | |
| dc.date.accessioned | 2023-07-19T14:04:50Z | |
| dc.date.available | 2023-07-19T14:04:50Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | We proposed a robust method of estimating covariance matrix in multivariate data set. The goal is to compare the proposed method with the most widely used robust methods (Minimum Volume Ellipsoid and Minimum Covariance Determinant) and the classical method (MLE) in detection of outliers at different levels and magnitude of outliers. The proposed robust method competes favourably well with both MVE and MCD and performed better than any of the two methods in detection of single or fewer outliers especially for small sample size and when the magnitude of outliers is relatively small. | en_US |
| dc.description.sponsorship | Self-sponsored | en_US |
| dc.identifier.citation | Scientific Annals of the Alexandru Ioan Cuza | en_US |
| dc.identifier.issn | 0379-7864 | |
| dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/11575 | |
| dc.language.iso | en | en_US |
| dc.publisher | University Press. University of Iasi, Romania | en_US |
| dc.relation.ispartofseries | 56;586 - 601 | |
| dc.subject | Covariance Matrix, Minimum Volume Ellipsoid (MVE), Minimum Covariance Determinant (MCD), Mahalanobis Distance, Optimality criteria. | en_US |
| dc.title | A Robust Method of Estimating Covariance Matrix in Multivariate Data Analysis | en_US |
| dc.type | Article | en_US |