scientific article; zbMATH DE number 2058049
From MaRDI portal
Publication:4456274
zbMath1034.62058MaRDI QIDQ4456274
Peter J. Rousseeuw, Katrien van Driessen
Publication date: 16 March 2004
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items
CVaR (superquantile) norm: stochastic case, Outlier detection using difference-based variance estimators in multiple regression, Estimating parameters of polynomial models with errors in variables and no additional information, Quantitative robustness of instance ranking problems, Least sum of squares of trimmed residuals regression, Robust joint modeling of mean and dispersion through trimming, Feasible robust estimator in restricted semiparametric regression models based on the LTS approach, Sparse least trimmed squares regression for analyzing high-dimensional large data sets, Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator, Robust ridge and robust Liu estimator for regression based on the LTS estimator, Regression Neural Networks with a Highly Robust Loss Function, Regression analysis: likelihood, error and entropy, Fast and robust bootstrap for LTS, An algorithm for computing exact least-trimmed squares estimate of simple linear regression with constraints, Robust regression diagnostics with data transformations, Finding approximate solutions to combinatorial problems with very large data sets using BIRCH, Econometric applications of high-breakdown robust regression techniques, Least trimmed squares in nonlinear regression under dependence, A resistant learning procedure for coping with outliers, Robust fitting of mixtures using the trimmed likelihood estimator, Multiple outliers detection in sparse high-dimensional regression, Extended least trimmed squares estimator in semiparametric regression models with correlated errors, Least trimmed squares ridge estimation in partially linear regression models, Computing the least quartile difference estimator in the plane, A critical overview of outlier detection methods
