Bayesian and maximin optimal designs for heteroscedastic regression models

DOI10.1002/cjs.5550330205zbMath1071.62066OpenAlexW2004319653MaRDI QIDQ5696345

Linda M. Haines, Dette, Holger, Lorens A. Imhof

Publication date: 18 October 2005

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

Full work available at URL: https://www.econstor.eu/bitstream/10419/49353/1/379083701.pdf

zbMATH Keywords

polynomial regression\(D\)-optimalityBayesian designmaximin designstandardized criterion

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15)

Related Items (8)

Some robust design strategies for percentile estimation in binary response models ⋮ V-optimal designs for heteroscedastic regression ⋮ Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models ⋮ A computational algorithm for selecting robust designs in safety and quality critical processes ⋮ Robust designs for misspecified logistic models ⋮ Locally D-optimal designs for multistage models and heteroscedastic polynomial regression models ⋮ A general approach to \(D\)-optimal designs for weighted univariate polynomial regression models ⋮ Designs for weighted least squares regression, with estimated weights

Cites Work

• Maximin efficient designs for estimating nonlinear aspects in linear models
• Optimal Bayesian designs for models with partially specified heteroscedastic structure
• A minimax algorithm for constructing optimal symmetrical balanced designs for a logistic regression model
• Bayesian experimental design: A review
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• Variance Function Estimation
• A unified approach to the construction of minimax designs
• A Resampling Procedure for Complex Survey Data
• Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors
• A Graphical Method for Finding Maximin Efficiency Designs
• Locally Optimal Designs for Estimating Parameters
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