A computationally efficient of robust mahalanobis distance based on MVV estimator

机译：基于MVV估计器的鲁棒马哈拉诺比斯距离的计算效率

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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.

机译：MCD是众所周知的多元稳健估计器。然而，由于目标函数的复杂性，即最小化协方差决定因素，估计器的计算并不简单，特别是对于大样本量。最近，引入了更简单和更快的替代目标函数。目标函数是使向量方差最小化，因此将生成称为最小向量方差（MVV）的估计量。本文进行了仿真研究，比较了两个估计量在目标函数计算中的运算效率以及算法迭代以收敛的效率。结果表明，无论大小数据集，MVV的计算效率均高于MCD。

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来源
《2015 International Symposium on Mathematical Sciences and Computing Research》|2015年|339-342|共4页
会议地点 Ipoh(MY)
作者
Hazlina Ali; Sharipah Soaad; Syed Yahaya; Zurni Omar;
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作者单位

Faculty of Quatitative Sciences, College of Art and Sciences, Universiti Utara Malaysia, Sintok Kedah;

Faculty of Quatitative Sciences, College of Art and Sciences, Universiti Utara Malaysia, Sintok Kedah;

Faculty of Quatitative Sciences, College of Art and Sciences, Universiti Utara Malaysia, Sintok Kedah;

Faculty of Quatitative Sciences, College of Art and Sciences, Universiti Utara Malaysia, Sintok Kedah;

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原文格式 PDF
正文语种 eng
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关键词
Computational efficiency; Covariance matrices; Algorithm design and analysis; Robustness; Linear programming; Convergence; Computational modeling;

机译：计算效率;协方差矩阵;算法设计与分析;稳健性;线性规划;收敛性;计算建模;

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A computationally efficient of robust mahalanobis distance based on MVV estimator

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