TFRE - A Tuning-Free Robust and Efficient Approach to High-Dimensional
Regression
Provide functions to estimate the coefficients in
high-dimensional linear regressions via a tuning-free and
robust approach. The method was published in 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(JASA’s discussion paper),
<doi:10.1080/01621459.2020.1840989>. See also 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, 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; 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>.
