Abstract
A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw’s minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 69-95 |
| Number of pages | 27 |
| Journal | Journal of Computational and Graphical Statistics |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1993 |
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Keywords
- Breakdown point
- Genetic algorithms
- Multivariate location and shape
- Robust estimation
- Simulated annealing
- Tabu search
ASJC Scopus subject areas
- Discrete Mathematics and Combinatorics
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Heuristic Search Algorithms for the Minimum Volume Ellipsoid. / Wood, David L.; Rocke, David M.
In: Journal of Computational and Graphical Statistics, Vol. 2, No. 1, 1993, p. 69-95.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Heuristic Search Algorithms for the Minimum Volume Ellipsoid
AU - Wood, David L.
AU - Rocke, David M
PY - 1993
Y1 - 1993
N2 - A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw’s minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.
AB - A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw’s minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.
KW - Breakdown point
KW - Genetic algorithms
KW - Multivariate location and shape
KW - Robust estimation
KW - Simulated annealing
KW - Tabu search
UR - http://www.scopus.com/inward/record.url?scp=0002102675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0002102675&partnerID=8YFLogxK
U2 - 10.1080/10618600.1993.10474600
DO - 10.1080/10618600.1993.10474600
M3 - Article
AN - SCOPUS:0002102675
VL - 2
SP - 69
EP - 95
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
SN - 1061-8600
IS - 1
ER -