Minimax A‐, c‐, and I‐optimal regression designs for models with heteroscedastic errors

DOI10.1002/cjs.11674OpenAlexW4200345827MaRDI QIDQ6059444

Julie Zhou, Unnamed Author

Publication date: 2 November 2023

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

Full work available at URL: https://doi.org/10.1002/cjs.11674

zbMATH Keywords

nonconvex optimizationgeneralized least squares estimatorminimax designL-optimalityrobust regression design

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• V-optimal designs for heteroscedastic regression
• D-optimum designs in multi-factor models with heteroscedastic errors
• A geometric characterization of \(c\)-optimal designs for heteroscedastic regression
• Maximin designs for exponential growth models and heteroscedastic polynomial models
• An algorithmic construction of optimal minimax designs for heteroscedastic linear models
• Minimax D-optimal designs for multivariate regression models with multi-factors
• Variations and extension of the convex-concave procedure
• An Introduction to Optimal Designs for Social and Biomedical Research
• Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors
• Optimal Designs for Rational Function Regression
• CVX‐based algorithms for constructing various optimal regression designs
• Optimal Design of Experiments
• Optimal designs for additive mixture model with heteroscedastic errors
• A new design criterion when heteroscedasticity is ignored

This page was built for publication: Minimax A‐, c‐, and I‐optimal regression designs for models with heteroscedastic errors