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- W2181899196 abstract "The most of the multivariate statistical methods heavily depend on two estimators of location and scatter. The sample mean and covariance which are most commonly used efficient estimators when the data belong to multivariate normal distribution. But they are extremely sensitive to outliers. Many multivariate datasets contain outliers and often they do not show up by simple visual inspection. It is quite easy to detect a single outlier by Mahalanobis distances. But, it is no longer valid for multiple outliers, because the multiple outliers do not necessarily have large Mahalanobis distance. Many robust estimators have been developed to obtain the reliable results while analysing the high dimensional data during the past decades. The main objective of such robust estimator is to find the optimal (h) data points out of n and then estimate the location and scatter. This paper proposes a similar procedure for the selection of optimal data points to estimate multivariate location and scatter along with MCD. The efficiency of the proposed procedure is compared with the most widely used robust estimators such as M, OGK, MVE, MCD and S with the classical estimator MLE in a real data set and the simulating environment in R is also presented." @default.
- W2181899196 created "2016-06-24" @default.
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- W2181899196 date "2014-01-01" @default.
- W2181899196 modified "2023-09-27" @default.
- W2181899196 title "Robust Procedure for Estimating Multivariate Location and Scatter" @default.
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