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Model-robust designs for quantile regression

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Design and Analysis of Experiments

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. Our methods, previously developed for approximate linear models, are modified so as to apply to non-linear responses. The results will be illustrated in a dose-response example.

This talk is part of the Isaac Newton Institute Seminar Series series.

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