Robust regression and outlier detection
Robust regression and outlier detection
Computing the Minimum Covariance Determinant Estimator (MCD) by simulated annealing
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
SIAM Journal on Scientific Computing
The feasible set algorithm for least median of squares regression
Computational Statistics & Data Analysis
Improved feasible solution algorithms for high breakdown estimation
Computational Statistics & Data Analysis
New algorithms for computing the least trimmed squares regression estimator
Computational Statistics & Data Analysis
Computing LTS Regression for Large Data Sets
Data Mining and Knowledge Discovery
Modern Applied Statistics with S
Modern Applied Statistics with S
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
Semiparametrically weighted robust estimation of regression models
Computational Statistics & Data Analysis
Benchmark testing of algorithms for very robust regression: FS, LMS and LTS
Computational Statistics & Data Analysis
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
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A drawback of robust statistical techniques is the increased computational effort often needed as compared to non-robust methods. Particularly, robust estimators possessing the exact fit property are NP-hard to compute. This means that-under the widely believed assumption that the computational complexity classes NP and P are not equal-there is no hope to compute exact solutions for large high dimensional data sets. To tackle this problem, search heuristics are used to compute NP-hard estimators in high dimensions. A new evolutionary algorithm that is applicable to different robust estimators is presented. Further, variants of this evolutionary algorithm for selected estimators-most prominently least trimmed squares and least median of squares-are introduced and shown to outperform existing popular search heuristics in difficult data situations. The results increase the applicability of robust methods and underline the usefulness of evolutionary algorithms for computational statistics.