Model-robust designs for quantile regression

• Autores: Linglong Kong, Douglas P. Wiens
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 110, Nº 509, 2015, págs. 233-245
• Idioma: inglés
• DOI: 10.1080/01621459.2014.969427
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• Resumen

  • 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.