Bias robust estimation of scale

DOI10.1214/aos/1176349161zbMath0787.62038OpenAlexW2011729884MaRDI QIDQ688401

Ruben H. Zamar, R. Douglas Martin

Publication date: 19 May 1994

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176349161

zbMATH Keywords

Monte Carlo studyrobust estimationlocation-scale familymedian of absolute residuals\(M\)-estimatesasymptotic functionalscontaminated Gaussian datacontamination neighborhoodfinite sample size mean squared error propertiesHuber's Proposal 2 joint estimateslocation residualsmaximum asymptotic biasmin-max bias robust estimates of scalescale estimatesshortest half of the data

Mathematics Subject Classification ID

Point estimation (62F10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (19)

Uniform asymptotics for robust location estimates when the scale is unknown ⋮ RobustM-estimators of scale: Minimax bias versus maximal variance ⋮ Robust and efficient estimation of the residual scale in linear regression ⋮ Some properties of the length of the shortest half ⋮ Change point detection with robust control chart ⋮ Uniform asymptotics for S- and MM-regression estimators ⋮ Globally robust inference for the location and simple linear regression models ⋮ Robust functional principal components: a projection-pursuit approach ⋮ Globally robust confidence intervals for simple linear regression ⋮ Globul robustness of location and dispersion estimates ⋮ Bias robustness of three median-based regression estimates. ⋮ M-Estimators of Scale with Minimum Gross Errors Sensitivity ⋮ Robust filtering of time series with trends ⋮ Bias-robustL-estimators of a scale parameter ⋮ A Measure of Directional Outlyingness With Applications to Image Data and Video ⋮ Comparisons of asymptotic biases and variances ofm-estimators of scale under asymmetric contamination ⋮ Maximum bias curves for robust regression with non-elliptical regressors ⋮ On the global robustness of generalized S-estimators ⋮ Efficient high-breakdown \(M\)-estimators of scale

This page was built for publication: Bias robust estimation of scale