JOURNAL ARTICLE

Robust Multivariate Outlier Labeling

Dyah Erny HerwindiatiMaman A. DjauhariMuhammad Mashuri

Year: 2007 Journal:   Communications in Statistics - Simulation and Computation Vol: 36 (6)Pages: 1287-1294   Publisher: Taylor & Francis

Abstract

A criterion for robust estimation of location and covariance matrix is considered, and its application in outlier labeling is discussed. This method, unlike the methods based on MVE and MCD, is applicable to large and high-dimension data sets. The method proposed here is also robust and has the same breakdown point as the MVE- and MCD-based methods. Furthermore, the computational complexity of the proposed method is significantly smaller than that of other methods.

Keywords:
Outlier Covariance matrix Dimension (graph theory) Covariance Computer science Multivariate statistics Point (geometry) Anomaly detection Estimation of covariance matrices Algorithm Pattern recognition (psychology) Data mining Artificial intelligence Mathematics Statistics Machine learning Combinatorics

Metrics

46
Cited By
1.57
FWCI (Field Weighted Citation Impact)
14
Refs
0.84
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Statistical Methods and Models
Physical Sciences →  Mathematics →  Statistics and Probability
Advanced Statistical Process Monitoring
Social Sciences →  Decision Sciences →  Statistics, Probability and Uncertainty
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability

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