| Name : R-TFRE
| |
| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp155.1.3
| Date : 2024-09-10 18:23:16
|
| Group : Development/Libraries/Other
| Source RPM : R-TFRE-0.1.0-lp155.1.3.src.rpm
|
| Size : 0.17 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression
|
Description :
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>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
