Wild bootstrap for quantile regression
成果类型：
Article
署名作者：
Feng, Xingdong; He, Xuming; Hu, Jianhua
署名单位：
Shanghai University of Finance & Economics; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asr052
发表日期：
2011
页码：
995999
关键词：
摘要：
The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on median regression is carried out to compare various bootstrap methods. With a simple finite-sample correction, the wild bootstrap is shown to account for general forms of heteroscedasticity in a regression model with fixed design points.
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