native_cpp_qlasso_fun_

q_mat

l_vec

b_vec0

w_vec

iter_max

iiter_max

b_eps

k_eps

p_eff

q_rank

c_eps

<p>This is internal use only function. Manual left blank on purpose.</p>

An efficient unified nonconvex penalized estimation algorithm for
Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit,
and Cox proportional hazard regression models.
The unified algorithm is implemented based on the convex concave procedure and
the algorithm can be applied to most of the existing nonconvex penalties.
The algorithm also supports convex penalty:
least absolute shrinkage and selection operator (LASSO).
Supported nonconvex penalties include
smoothly clipped absolute deviation (SCAD),
minimax concave penalty (MCP), truncated LASSO penalty (TLP),
clipped LASSO (CLASSO), sparse ridge (SRIDGE),
modified bridge (MBRIDGE) and modified log (MLOG).
For high-dimensional data (data set with many variables),
the algorithm selects relevant variables producing a parsimonious regression model.
Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>,
Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>,
Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>.
(This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine
Graziadio Business School and the National Research Foundation of Korea.)

Dongshin Kim

ncpen

Unified Algorithm for Non-convex Penalized Estimation for
Generalized Linear Models

native_cpp_qlasso_fun_: Native QLASSO function.

Description

Usage

Arguments

Value