scholarly journals Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

2004 ◽  
Vol 44 (4) ◽  
pp. 625-638 ◽  
Author(s):  
Johanna Hardin ◽  
David M Rocke
Keyword(s):  
Outlier Detection ◽  
Minimum Covariance Determinant ◽  
Minimum Covariance Determinant Estimator ◽  
Multiple Cluster

scholarly journals Asymptotics for the Minimum Covariance Determinant Estimator

1993 ◽  
Vol 21 (3) ◽  
pp. 1385-1400 ◽  
Author(s):  
R. W. Butler ◽  
P. L. Davies ◽  
M. Jhun
Keyword(s):  
Minimum Covariance Determinant ◽  
Minimum Covariance Determinant Estimator

A Robust Multivariate Outlier Detection Method for Detection of Securities Fraud

2020 ◽  
Vol 7 (3) ◽  
pp. 12-29
Author(s):  
M. Fevzi Esen
Keyword(s):  
Outlier Detection ◽  
Insider Trading ◽  
Business Intelligence ◽  
Abnormal Returns ◽  
Detection Methods ◽  
Financial Fraud ◽  
Securities Markets ◽  
Securities Fraud ◽  
Minimum Covariance Determinant ◽  
Minimum Volume Ellipsoid

Insider trading is one the most common deceptive trading practice in securities markets. Data mining appears as an effective approach to tackle the problems in fraud detection with high accuracy. In this study, the authors aim to detect outlying insider transactions depending on the variables affecting insider trading profitability. 1,241,603 sales and purchases of insiders, which range from 2010 to 2017, are analyzed by using classical and robust outlier detection methods. They computed robust distance scores based on minimum volume ellipsoid, Stahel-Donoho, and fast minimum covariance determinant estimators. To investigate the outlying observations that are likely to be fraudulent, they employ event study analysis to measure abnormal returns of outlying transactions. The results are compared to the abnormal returns of non-outlying transactions. They find that outlying transactions gain higher abnormal returns than transactions that are not flagged as outliers. Business intelligence and analytics may be a useful strategy for detecting and preventing of financial fraud for companies.

scholarly journals Comparison of Multivariate Outlier Detection Methods for Nearly Elliptical Distributions

2020 ◽  
Vol 49 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Kazumi Wada ◽  
Mariko Kawano ◽  
Hiroe Tsubaki
Keyword(s):  
Outlier Detection ◽  
Survey Data ◽  
Detection Methods ◽  
Distributed Data ◽  
Computationally Efficient ◽  
Minimum Covariance Determinant ◽  
Practical Applications ◽  
Medium Enterprise ◽  
Enterprise Survey ◽  
T Distribution

In this paper, the performance of outlier detection methods has been evaluated with symmetrically distributed datasets. We choose four estimators, viz. modified Stahel-Donoho (MSD) estimators, blocked adaptive computationally efficient outlier nominators, minimum covariance determinant estimator obtained by a fast algorithm, and nearest-neighbour variance estimator, which are known for their good performance with elliptically distributed data, for practical applications in national survey data processing. We adopt the data model of multivariate skew-t distribution, of which only the direction of the main axis is skewed and contaminated with outliers following another probability distribution for evaluation. We conducted Monte Carlo simulation under the data distribution to compare the performance of outlier detection. We also explore the applicability of the selected methods for several accounting items in small and medium enterprise survey data. Accordingly, it was found that the MSD estimators are the most suitable.

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