Dual regression

• 成果类型: Article
• 署名作者: Spady, R. H.; Stouli, S.
• 署名单位: University of Oxford; University of Bristol
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx074
• 发表日期: 2018
• 页码: 118
• 关键词: Quantile regression; inference; CURVES

摘要

We propose dual regression as an alternative to quantile regression for the global estimation of conditional distribution functions. Dual regression provides the interpretational power of quantile regression while avoiding the need to repair intersecting conditional quantile surfaces. We introduce a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models, and use it to specify and estimate a flexible class of conditional distribution functions. We present asymptotic theory for the corresponding empirical dual regression process.