Abstract
New insights are given into why the problem of detecting multivariate outliers can be difficult and why the difficulty increases with the dimension of the data. Significant improvements in methods for detecting outliers are described, and extensive simulation experiments demonstrate that a hybrid method extends the practical boundaries of outlier detection capabilities. Based on simulation results and examples from the literature, the question of what levels of contamination can be detected by this algorithm as a function of dimension, computation time, sample size, contamination fraction, and distance of the contamination from the main body of data is investigated. Software to implement the methods is available from the authors and STATLIB.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1047-1061 |
| Number of pages | 15 |
| Journal | Journal of the American Statistical Association |
| Volume | 91 |
| Issue number | 435 |
| State | Published - Sep 1996 |
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Keywords
- Heuristic search
- M estimation
- Minimum covariance determinant
- S estimation
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
Cite this
Identification of outliers in multivariate data. / Rocke, David M; Woodruff, David L.
In: Journal of the American Statistical Association, Vol. 91, No. 435, 09.1996, p. 1047-1061.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Identification of outliers in multivariate data
AU - Rocke, David M
AU - Woodruff, David L.
PY - 1996/9
Y1 - 1996/9
N2 - New insights are given into why the problem of detecting multivariate outliers can be difficult and why the difficulty increases with the dimension of the data. Significant improvements in methods for detecting outliers are described, and extensive simulation experiments demonstrate that a hybrid method extends the practical boundaries of outlier detection capabilities. Based on simulation results and examples from the literature, the question of what levels of contamination can be detected by this algorithm as a function of dimension, computation time, sample size, contamination fraction, and distance of the contamination from the main body of data is investigated. Software to implement the methods is available from the authors and STATLIB.
AB - New insights are given into why the problem of detecting multivariate outliers can be difficult and why the difficulty increases with the dimension of the data. Significant improvements in methods for detecting outliers are described, and extensive simulation experiments demonstrate that a hybrid method extends the practical boundaries of outlier detection capabilities. Based on simulation results and examples from the literature, the question of what levels of contamination can be detected by this algorithm as a function of dimension, computation time, sample size, contamination fraction, and distance of the contamination from the main body of data is investigated. Software to implement the methods is available from the authors and STATLIB.
KW - Heuristic search
KW - M estimation
KW - Minimum covariance determinant
KW - S estimation
UR - http://www.scopus.com/inward/record.url?scp=0030344143&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030344143&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0030344143
VL - 91
SP - 1047
EP - 1061
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 435
ER -