conquer-package: Conquer: Convolution-Type Smoothed Quantile Regression

Description Author(s) References

Description

Fast and accurate convolution-type smoothed quantile regression. Implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Construct confidence intervals for regression coefficients using multiplier bootstrap.

Author(s)

Xuming He <xmhe@umich.edu>, Xiaoou Pan <xip024@ucsd.edu>, Kean Ming Tan <keanming@umich.edu>, and Wen-Xin Zhou <wez243@ucsd.edu>

References

Barzilai, J. and Borwein, J. M. (1988). Two-point step size gradient methods. IMA J. Numer. Anal. 8 141–148.

Fernandes, M., Guerre, E. and Horta, E. (2019). Smoothing quantile regressions. J. Bus. Econ. Statist., in press.

He, X., Pan, X., Tan, K. M., and Zhou, W.-X. (2020). Smoothed quantile regression for large-scale inference. Preprint.

Horowitz, J. L. (1998). Bootstrap methods for median regression models. Econometrica 66 1327–1351.

Koenker, R. (2005). Quantile Regression. Cambridge University Press, Cambridge.

Koenker, R. Package "quantreg".

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

Pan, X. and Zhou, W.-X. (2020). Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design. Information and Inference: A Journal of the IMA, to appear.


conquer documentation built on Aug. 27, 2020, 9:07 a.m.