A Fast Algorithm for the Minimum Covariance Determinant Estimator
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Summary
For small datasets, FAST-MCD typically finds the exact MCD, whereas for larger datasets it gives more accurate results than existing algorithms and is faster by orders.
- Type
- article
- Published
- 1999-08-01
- Cited by
- 2,858
- References
- 38
- OpenAlex
- https://openalex.org/W1980262437
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:45023291
Keywords
Mahalanobis distance, Covariance, Estimator, Algorithm, Computation
References
- The lower bound method in probit regression
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- A minimal characterization of the covariance matrix
- The feasible solution algorithm for the minimum covariance determinant estimator in multivariate data
- Robust statistics: the approach based on influence functions
- Computable Robust Estimation of Multivariate Location and Shape in High Dimension Using Compound Estimators
- Asymptotics for the minimum covariance determinant estimator
- The asymptotics of Rousseeuw's minimum volume ellipsoid estimator
- Identification of Outliers
- Exact iterative computation of the robust multivariate minimum volume ellipsoid estimator
- Heuristic Search Algorithms for the Minimum Volume Ellipsoid
- Unmasking Multivariate Outliers and Leverage Points
- On One-Step GM Estimates and Stability of Inferences in Linear Regression
- A Bounded Influence, High Breakdown, Efficient Regression Estimator
- Identification of Outliers in Multivariate Data
- Influences of Upwelling, Ocean Temperature, and Smolt Abundance on Marine Survival of Coho Salmon (Oncorhynchus kisutch) in the Oregon Production Area
- Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator
- High-Breakdown Linear Discriminant Analysis
- Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance Matrices
- Least Median of Squares Regression
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- Robust Estimation in Multivariate Control Charts for Individual Observations
- A Comparative Study of Outlier Detection Procedures in Multiple Linear Regression
- Recommended Practices for Editing and Imputation in Cross-Sectional Business Surveys.
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