Model-Robust Designs for Quantile Regression

• Activity Number: 165
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Douglas Wiens* and Linglong Kong
• Companies: University of Alberta and University of Alberta
• Keywords: Minimax design ; Asymptotic mean squared error ; Genetic algorithm ; Regression quantiles ; Uniformity

Abstract

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 nonlinear responses. The results will be illustrated in a dose response example.