Designs for approximately linear regression: Maximizing the minimum coverage probability of confidence ellipsoids

DOI10.2307/3315658zbMath0772.62044OpenAlexW2171345898MaRDI QIDQ4201519

Douglas P. Wiens

Publication date: 25 August 1993

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

Full work available at URL: https://doi.org/10.2307/3315658

zbMATH Keywords

robustnessbias\(D\)-optimalitynonlinear disturbanceapproximately linear modelconfidence ellipsoidfitted response surfacemaximin coverage probability

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Optimal statistical designs (62K05)

Related Items (9)

Robust designs for approximately linear regression: \(M\)-estimated parameters ⋮ for misspecified regression models ⋮ Robust designs for approximately polynomial regression ⋮ V-optimal designs for heteroscedastic regression ⋮ Existence and symmetry of minimax regression designs ⋮ New optimal design criteria for regression models with asymmetric errors ⋮ \(P\)-optimal robust designs for multiresponse approximately linear regression ⋮ Minimax regression designs for approximately linear models with autocorrelated errors ⋮ A two-stage strategy for the construction of d-optimal experimental designs

Cites Work

• Robust minimax designs for multiple linear regression
• Optimal designs for regression models with possible bias
• Robust designs for nearly linear regression
• Optimal robust designs: Linear regression in \(R^ k\).
• Designs for approximately linear regression: Two optimality properties of uniform designs
• Minimax designs for approximately linear regression
• Linear estimation for approximately linear models
• On the Nonrandomized Optimality and Randomized Nonoptimality of Symmetrical Designs
• A Basis for the Selection of a Response Surface Design

This page was built for publication: Designs for approximately linear regression: Maximizing the minimum coverage probability of confidence ellipsoids