Model-Robust Designs for Quantile Regression

• 成果类型: Article
• 署名作者: Kong, Linglong; Wiens, Douglas P.
• 署名单位: University of Alberta
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.969427
• 发表日期: 2015
• 页码: 233-245
• 关键词: growth

摘要

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.