Quantile regression in reproducing kernel Hilbert spaces

• 成果类型: Article
• 署名作者: Li, Youjuan; Liu, Yufeng; Zhu, Ji
• 署名单位: University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000979
• 发表日期: 2007
• 页码: 255-268
• 关键词: survival; CURVES; error

摘要

In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptotic theory for the KQR model.