Model-Robust Designs for Quantile Regression

Linglong Kong and Douglas P. Wiens

Journal of the American Statistical Association, 2015, vol. 110, issue 509, 233-245

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. The methods are illustrated by example, and in a case study in which they are applied to growth charts.

Date: 2015
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DOI: 10.1080/01621459.2014.969427

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