Optimal designs for robust estimation in conditionally contaminated linear models

DOI10.1016/0378-3758(92)00155-WzbMath0803.62066OpenAlexW2050057539MaRDI QIDQ1329692

Christine H. Müller

Publication date: 5 January 1995

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(92)00155-w

zbMATH Keywords

tracecovariance matrixrobust estimationasymptotically linear estimatorsinfluence functionsHuber estimatorfinite supportbounded biasgeneralization of A-optimalityHampel-Krasker estimatorinfinitesimal conditionally contaminated normal linear modelinfinitesimal contamination neighbourhoodlinear aspectoptimal influence functionsoptimally robust

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Optimal statistical designs (62K05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (3)

Breakdown points for designed experiments ⋮ Optimal bounded influence regression and scale m-estimators in the context of experimental desing ⋮ Robust Linear Calibration

Cites Work

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• Robust regression based on infinitesimal neighbourhoods
• Optimum robust estimation of linear aspects in conditionally contaminated linear models
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Change-of-variance sensitivities in regression analysis
• Estimation in Linear Regression Models with Disparate Data Points
• Robust Statistics

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