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mbQuantile

Multiplier Bootstrap for Quantile Regression

Description

This is simulation code for the paper "Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design". See here for the paper. The code can reproduce numerical results in Section 3 and Appendix B.

Specifically, to duplicate results in Section 3.1 and Appendix B.1, run the file mb_ci.R to construct confidence intervals, to replicate results in Section 3.2 and Appendix B.2, run the files mb_ht.R and mb_pc.R to conduct hypothesis tests and draw power curves. In all the files, we allow the following various settings, with details stated in the paper:

  • Model type: homoscedastic model / heteroscedastic model
  • Error distribution: student's t / normal mixture type I / normal mixture type II
  • Covariates design: independent / weakly correlated / equally correlated

Authors

Xiaoou Pan xip024@ucsd.edu, Wen-Xin Zhou wez243@ucsd.edu

Main reference

Chen, K., Ying, Z., Zhang, H. and Zhao, L. (2008). Analysis of least absolute deviation. Biometrika 95 107–122. Paper

Feng, X., He, X. and Hu, J. (2011). Wild bootstrap for quantile regression. Biometrika 98 995–999. Paper

Koenker, R. (2005). Quantile Regression. Cambridge Univ. Press, Cambridge. Book

Koenker, R. (2019). Package "quantreg". CRAN

Koenker, R. and Bassett, G. (1978). Regression quantiles. Econometrica 46 33-50. Paper

Pan, X. and Zhou, W.-X. (2019). Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design. Information and Inference: A Journal of the IMA, to appear. Paper

Parzen, M. I., Wei, L. J. and Ying, Z. (1994). A resampling method based on pivotal estimating functions. Biometrika 81 341–350. Paper

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