Robust extrapolation designs and weights for biased regression models with heteroscedastic errors

DOI10.2307/3316129zbMath0959.62064OpenAlexW2030468176MaRDI QIDQ4488790

Douglas P. Wiens, Zhide Fang

Publication date: 2 May 2001

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3316129

zbMATH Keywords

predictionbioassaysD-optimalityweighted least squareshomoscedasticitydose-responseQ-optimalityapproximate linearity

Mathematics Subject Classification ID

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

Related Items (11)

Extrapolation designs with constraints ⋮ Model robust designs for survival trials ⋮ Optimal Robust Designs of Step-Stress Accelerated Life Testing Experiments for Proportional Hazards Models ⋮ Applications and Implementations of Continuous Robust Designs ⋮ Robust prediction and extrapolation designs for misspecified generalized linear regression models ⋮ Robust designs for one-point extrapolation ⋮ Robust prediction and extrapolation designs for censored data ⋮ Minimax designs for linear regression models with bias in a reproducing kernel Hilbert space in a discrete set ⋮ Robust prediction and extrapolation designs for nonlinear regression with imprecision ⋮ Robust sequential designs for nonlinear regression ⋮ Robust extrapolation designs for biased polynomial models

Cites Work

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• A unified approach to the construction of minimax designs
• Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors
• Optimal Spacing and Weighting in Polynomial Prediction
• Optimal Designs for Multivariate Polynomial Extrapolation
• A Basis for the Selection of a Response Surface Design

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