Some robust design strategies for percentile estimation in binary response models

Stefanie Biedermann · Holger Dette · Andrey Pepelyshev

29 citations0/0 files storedCanadian Journal of Statistics

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Optimal Experimental Design Methods Statistical Methods in Clinical Trials Advanced Statistical Methods and Models

Abstract

Abstract For the problem of percentile estimation of a quantal response curve, the authors determine multiobjective designs which are robust with respect to misspecifications of the model assumptions. They propose a maximin approach based on efficiencies which leads to designs that are simultaneously efficient with respect to various choices of link functions and parameter regions. Furthermore, the authors deal with the problems of designing model and percentile robust experiments. They give various examples of such designs, which are calculated numerically.

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1993Optimal design of experiments 1953Locally Optimal Designs for Estimating Parameters 1980Optimal Design 1989Optimal Bayesian design applied to logistic regression experiments 1994Optimal Design of Experiments 1992The Use of a Canonical Form in the Construction of Locally Optimal Designs for Non-Linear Problems 1997Designing Experiments with Respect to ‘Standardized’ Optimality Criteria 1985Efficient Sequential Designs with Binary Data

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