coxphMIC: Sparse Estimation of Cox Proportional Hazards Models via
Approximated Information Criterion
Sparse estimation for Cox PH models is done via
Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe,
Fan, and Zhang (2016) <doi:10.1111/biom.12484>. MIC mimics the best
subset selection using a penalized likelihood approach yet with no need
of a tuning parameter. The problem is further reformulated with a
re-parameterization step so that it reduces to one unconstrained non-convex
yet smooth programming problem, which can be solved efficiently. Furthermore,
the re-parameterization tactic yields an additional advantage in terms of
circumventing post-selection inference.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.1.0), stats (≥ 3.2.5), graphics (≥ 3.2.5), utils (≥
3.2.5) |
| Imports: |
survival (≥ 2.38), numDeriv (≥ 2014.2-1) |
| Published: |
2017-04-26 |
| Author: |
Xiaogang Su and Razieh Nabi Abdolyousefi |
| Maintainer: |
Xiaogang Su <xiaogangsu at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
coxphMIC results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=coxphMIC
to link to this page.