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 |
| Reference manual: | coxphMIC.pdf |
| Package source: | coxphMIC_0.1.0.tar.gz |
| Windows binaries: | r-devel: coxphMIC_0.1.0.zip, r-release: coxphMIC_0.1.0.zip, r-oldrel: coxphMIC_0.1.0.zip |
| macOS binaries: | r-release: coxphMIC_0.1.0.tgz, r-oldrel: coxphMIC_0.1.0.tgz |
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