Some robust design strategies for percentile estimation in binary response models

DOI10.1002/cjs.5550340404zbMath1115.62069OpenAlexW1963676859MaRDI QIDQ5295955

Stefanie Biedermann, Andrey Pepelyshev, Dette, Holger

Publication date: 31 July 2007

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

Full work available at URL: https://doi.org/10.1002/cjs.5550340404

zbMATH Keywords

psychophysicsquantal response curve

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Generalized linear models (logistic models) (62J12) Robust parameter designs (62K25)

Related Items (12)

A decoupling approach for time-dependent robust optimization with application to power semiconductor devices ⋮ Integral approximations for computing optimum designs in random effects logistic regression models ⋮ Robust and efficient estimation of effective dose ⋮ On generalized multinomial models and joint percentile estimation ⋮ Robust designs for generalized linear mixed models with possible model misspecification ⋮ Response-adaptive dose-finding under model uncertainty ⋮ Robust designs for probability estimation in binary response experiments ⋮ Design issues related to allocation of experimental units with known covariates into two treatment groups ⋮ Compound optimal designs for percentile estimation in dose-response models with restricted design intervals ⋮ A robust treatment of a dose–response study ⋮ Robust designs for generalized linear models with possible overdispersion and misspecified link functions ⋮ Sequential designs for repeated-measures experiments

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Optimal Bayesian design applied to logistic regression experiments
• 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
• Efficient Sequential Designs With Binary Data
• A Resampling Procedure for Complex Survey Data
• Experimental design in a class of models
• Robust and Efficient Designs for the Michaelis–Menten Model
• MULTIPLE-OBJECTIVE DESIGNS IN A DOSE-RESPONSE EXPERIMENT
• Minimax D‐Optimal Designs for the Logistic Model
• Bayesian and maximin optimal designs for heteroscedastic regression models
• Locally Optimal Designs for Estimating Parameters

This page was built for publication: Some robust design strategies for percentile estimation in binary response models