Ali, Hazlina and Syed Yahaya, Sharipah Soaad and Omar, Zurni (2015) A computationally efficient of robust mahalanobis distance based on MVV estimator. In: International Symposium on Mathematical Sciences and Computing Research (iSMSC), 19-20 May 2015, Hotel Casuarina@Meru, Bandar Meru Raya, Ipoh, Perak Darul Ridzuan, MALAYSIA.

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Abstract

MCD is a well-known multivariate robust estimator. However, the computation of the estimator is not simple especially for large sample size due to the complexity of the objective function i.e. minimizing covariance determinant. Recently, an alternative objective function which is simpler and faster was introduced. The objective function is to minimize vector variance, which consequently will generate the estimator known as minimum vector variance (MVV). In this paper, a simulation study was conducted to compare the computational efficiency of the two estimators with regards to the number of operations in the computation of objective function and also iterations of the algorithm to convergence. The result showed that the computational efficiency of MVV is higher than MCD for small or large data set.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Computational efficiency, Covariance matrices, Algorithm design and analysis, Robustness, Linear programming, Convergence, Computational modeling
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Quantitative Sciences
Depositing User:	Dr. Hazlina Ali
Date Deposited:	16 Apr 2017 02:59
Last Modified:	16 Apr 2017 02:59
URI:	https://repo.uum.edu.my/id/eprint/21571

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