Abstract
This paper reviews the state of the art in the computation of robust estimates of multivariate location and shape using combinatorial estimators such as the minimum volume ellipsoid (MVE) and iterative M‐ and S‐estimators. We also present new results on the behavior of M‐ and S‐estimators in the presence of different types of outliers, and give the first computational evidence on compound estimators that use the MVE as a starting point for an S‐estimator. Problems with too many data points in too many dimensions cannot be handled by any available technology; however, the methods presented in this paper substantially extend the size of problem that can be successfully handled.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 27-42 |
| Number of pages | 16 |
| Journal | Statistica Neerlandica |
| Volume | 47 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1993 |
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Keywords
- breakdown
- genetic algorithms
- heuristic search
- minimum volume ellipsoid
- M‐estimators
- outliers
- simulated anneating
- tabu search
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Computation of robust estimates of multivariate location and shape. / Rocke, David M; Woodruff, D. L.
In: Statistica Neerlandica, Vol. 47, No. 1, 1993, p. 27-42.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Computation of robust estimates of multivariate location and shape
AU - Rocke, David M
AU - Woodruff, D. L.
PY - 1993
Y1 - 1993
N2 - This paper reviews the state of the art in the computation of robust estimates of multivariate location and shape using combinatorial estimators such as the minimum volume ellipsoid (MVE) and iterative M‐ and S‐estimators. We also present new results on the behavior of M‐ and S‐estimators in the presence of different types of outliers, and give the first computational evidence on compound estimators that use the MVE as a starting point for an S‐estimator. Problems with too many data points in too many dimensions cannot be handled by any available technology; however, the methods presented in this paper substantially extend the size of problem that can be successfully handled.
AB - This paper reviews the state of the art in the computation of robust estimates of multivariate location and shape using combinatorial estimators such as the minimum volume ellipsoid (MVE) and iterative M‐ and S‐estimators. We also present new results on the behavior of M‐ and S‐estimators in the presence of different types of outliers, and give the first computational evidence on compound estimators that use the MVE as a starting point for an S‐estimator. Problems with too many data points in too many dimensions cannot be handled by any available technology; however, the methods presented in this paper substantially extend the size of problem that can be successfully handled.
KW - breakdown
KW - genetic algorithms
KW - heuristic search
KW - minimum volume ellipsoid
KW - M‐estimators
KW - outliers
KW - simulated anneating
KW - tabu search
UR - http://www.scopus.com/inward/record.url?scp=84995007758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995007758&partnerID=8YFLogxK
U2 - 10.1111/j.1467-9574.1993.tb01404.x
DO - 10.1111/j.1467-9574.1993.tb01404.x
M3 - Article
AN - SCOPUS:84995007758
VL - 47
SP - 27
EP - 42
JO - Statistica Neerlandica
JF - Statistica Neerlandica
SN - 0039-0402
IS - 1
ER -