Publikation:

A Cluster-Based Outlier Detection Scheme for Multivariate Data

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2015

Autor:innen

Jobe, J. Marcus

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https://doi.org/10.1080/01621459.2014.983231
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Published

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Journal of the American Statistical Association. 2015, 110(512), pp. 1543-1551. ISSN 0162-1459. eISSN 1537-274X. Available under: doi: 10.1080/01621459.2014.983231

Zusammenfassung

Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate data set of interest. To overcome this masking effect, we propose a computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw’s minimum covariance determinant method with a multi-step cluster-based algorithm that initially filters out potential masking points. Compared to the most robust procedures, simulation studies show that our new method is better for outlier detection. Additional real data comparisons are given.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

Breakdown point; Size and power; Kernel estimator; Bandwidth

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ISO 690JOBE, J. Marcus, Michael POKOJOVY, 2015. A Cluster-Based Outlier Detection Scheme for Multivariate Data. In: Journal of the American Statistical Association. 2015, 110(512), pp. 1543-1551. ISSN 0162-1459. eISSN 1537-274X. Available under: doi: 10.1080/01621459.2014.983231
BibTex
@article{Jobe2015Clust-29413,
  year={2015},
  doi={10.1080/01621459.2014.983231},
  title={A Cluster-Based Outlier Detection Scheme for Multivariate Data},
  number={512},
  volume={110},
  issn={0162-1459},
  journal={Journal of the American Statistical Association},
  pages={1543--1551},
  author={Jobe, J. Marcus and Pokojovy, Michael}
}
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