Wild bootstrap for quantile regression.

  title={Wild bootstrap for quantile regression.},
  author={Xingdong Feng and Xuming He and Jianhua Hu},
  volume={98 4},
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… CONTINUE READING
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