Quadratic mixed integer programming and support vectors for deleting outliers in robust regression

DOI10.1007/s10479-008-0412-4zbMath1163.90675OpenAlexW1991576099MaRDI QIDQ1026582

J. Blot

Publication date: 25 June 2009

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-008-0412-4

zbMATH Keywords

mixed integer programmingrobust regressionpenalty methodleast trimmed squaresidentifying outlierssupport vectors machine

Mathematics Subject Classification ID

Mixed integer programming (90C11)

Related Items (8)

Optimization techniques for robust multivariate location and scatter estimation ⋮ Simultaneous feature selection and outlier detection with optimality guarantees ⋮ Weighted relaxed support vector machines ⋮ A robust regression based on weighted LSSVM and penalized trimmed squares ⋮ Multi-criteria optimization in regression ⋮ Robust subset selection ⋮ Outlier Detection in Time Series via Mixed-Integer Conic Quadratic Optimization ⋮ Moving-horizon estimation with guaranteed robustness for discrete-time linear systems and measurements subject to outliers

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• A Fast Procedure for Outlier Diagnostics in Large Regression Problems
• Robust Statistics
• New algorithms for computing the least trimmed squares regression estimator

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