R-optimal designs for multi-factor models with heteroscedastic errors

DOI10.1007/s00184-017-0624-1zbMath1416.62438OpenAlexW2741744991MaRDI QIDQ1683640

Rong-Xian Yue, Lei He

Publication date: 1 December 2017

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-017-0624-1

zbMATH Keywords

additive modelsheteroscedastic errorsR-optimalityKronecker product modelmulti-factor regression

Mathematics Subject Classification ID

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

Related Items (7)

Optimal designs for multi-factor nonlinear models based on the second-order least squares estimator ⋮ Elfving's theorem for \(R\)-optimality of experimental designs ⋮ Bayesian optimal designs for multi-factor nonlinear models ⋮ Locally \(D\)-optimal designs for heteroscedastic polynomial measurement error models ⋮ Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models ⋮ \(R\)-optimal designs for trigonometric regression models ⋮ Optimal designs for heteroscedastic regression models with two parameters

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Algorithmic construction of \(R\)-optimal designs for second-order response surface models
• A note on \(R\)-optimal designs for multiresponse models
• A note on \(R\)-optimal designs for multi-factor models
• D-optimum designs in multi-factor models with heteroscedastic errors
• \(G\)-optimal designs for multi-factor experiments with heteroscedastic errors
• Optimal Bayesian designs for models with partially specified heteroscedastic structure
• Optimal discrimination designs for multifactor experiments
• Optimum designs for multi-factor models
• Optimal designs for a one-way layout with covariates
• Design optimality in multi-factor generalized linear models in the presence of an unrestricted quantitative factor
• A general approach to \(D\)-optimal designs for weighted univariate polynomial regression models
• A-optimal designs for heteroscedastic multifactor regression models
• Optimal design for additive partially nonlinear models
• An Introduction to Optimal Designs for Social and Biomedical Research
• Locally and maximin optimal designs for multi-factor nonlinear models
• Minimax Designs in Two Dimensional Regression
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• A new design criterion when heteroscedasticity is ignored

This page was built for publication: R-optimal designs for multi-factor models with heteroscedastic errors