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.

Getting started

Package details

AuthorXiaogang Su and Razieh Nabi Abdolyousefi
MaintainerXiaogang Su <[email protected]>
LicenseGPL-2
Version0.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("coxphMIC")

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coxphMIC documentation built on May 29, 2017, 11:05 a.m.