TFRE_python: A Tuning-Free Robust and Efficient Approach to High-dimensional Regression

This Python package provides functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Lan Wang, Bo Peng, Jelena Bradic, Runze Li and Yunan Wu (2020) A tuning-free robust and efficient approach to high-dimensional regression. Journal of the American Statistical Association, 115, 1700-1714 (JASA’s discussion paper). See also Lan Wang, Bo Peng, Jelena Bradic, Runze Li and Yunan Wu (2020), Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729.

You can preview the package documentation here.

To install the package, please run the following command in Terminal:

pip install git+https://github.com/yunanwu123/TFRE_python

This package requires the C++ template library eigen3. Please download it before installation.

Reference

Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), A Tuning-free Robust and Efficient Approach to High-dimensional Regression, Journal of the American Statistical Association, 115:532, 1700-1714, doi:10.1080/01621459.2020.1840989.

Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), Rejoinder to 'A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression', Journal of the American Statistical Association, 115:532, 1726-1729, doi:10.1080/01621459.2020.1843865.

Peng, B. and Wang, L. (2015), An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression, Journal of Computational and Graphical Statistics, 24:3, 676-694, doi:10.1080/10618600.2014.913516.

Clémençon, S., Colin, I., and Bellet, A. (2016), Scaling-up empirical risk minimization: optimization of incomplete u-statistics, The Journal of Machine Learning Research, 17(1):2682–2717, URL: https://jmlr.org/papers/v17/15-012.html.

Fan, J. and Li, R. (2001), Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, 96:456, 1348-1360, doi:10.1198/016214501753382273.