Matrix analysis
Computational Statistics & Data Analysis
Improved feasible solution algorithms for high breakdown estimation
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Using combinatorial optimization in model-based trimmed clustering with cardinality constraints
Computational Statistics & Data Analysis
RelaxMCD: Smooth optimisation for the Minimum Covariance Determinant estimator
Computational Statistics & Data Analysis
Robust online signal extraction from multivariate time series
Computational Statistics & Data Analysis
Robust data clustering by learning multi-metric Lq-norm distances
Expert Systems with Applications: An International Journal
The Gaussian rank correlation estimator: robustness properties
Statistics and Computing
Using robust dispersion estimation in support vector machines
Pattern Recognition
Hi-index | 5.23 |
In modern statistics the robust estimation of parameters is a central problem, i.e., an estimation that is not or only slightly affected by outliers in the data. The minimum covariance determinant (MCD) estimator (J. Amer. Statist. Assoc. 79 (1984) 871) is probably one of the most important robust estimators of location and scatter. The complexity of computing the MCD, however, was unknown and generally thought to be exponential even if the dimensionality of the data is fixed.Here we present a polynomial time algorithm for MCD for fixed dimension of the data. In contrast we show that computing the MCD-estimator is NP-hard if the dimension varies.