print.coxphMIC

an object of <code>coxphMIC</code> class.

the minimal number of significant digits. See <code><a rd-options="base" href="/link/print.default?package=coxphMIC&version=0.1.0&to=base" data-mini-rdoc="base::print.default">print.default</a></code>.

digits

further arguments passed to or from other methods.

The Generic <code>print</code> Function for Object of <code>coxphMIC</code> Class

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.

Xiaogang Su

coxphMIC

Sparse Estimation of Cox Proportional Hazards Models via
Approximated Information Criterion

print.coxphMIC function

the minimal number of significant digits. See <code><a rd-options='base' href='print.default'>print.default</a></code>.

print.coxphMIC: The Generic

Description

Usage

Arguments

Value

Details

References

See Also