典型文献
Outlier Detection via a Block Diagonal Product Estimator
文献摘要：
Outlier detection is a fundamental topic in robust statistics.Traditional outlier detection methods try to find a clean subset of given size,which is used to estimate the location vector and scatter matrix,and the outliers can be flagged by the Mahalanobis distance.However,methods such as the minimum covariance determinant approach cannot be applied directly to high-dimensional data,especially when the dimension of the sample is greater than the sample size.A novel fast detection procedure based on a block diagonal partition is proposed,and the asymptotic distribution of the modified Mahalanobis distance is obtained.The authors verify the specificity and sensitivity of this procedure by simulation and real data analysis in high-dimensional settings.
文献关键词：
中图分类号：
作者姓名：
LI Chikun;JIN Baisuo
作者机构：
School of Management,University of Science and Technology of China,Hefei 230026,China
文献出处：
引用格式：
[1]LI Chikun;JIN Baisuo-.Outlier Detection via a Block Diagonal Product Estimator)[J].系统科学与复杂性学报（英文版）,2022(05):1929-1943
A类：
Diagonal,flagged
B类：
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AB值：
0.644547
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