A Unified Algorithm for the Non-Convex Penalized Estimation: The ncpen Package

被引:0
|
作者
Kim, Dongshin [1 ]
Lee, Sangin [2 ]
Kwon, Sunghoon [3 ]
机构
[1] Pepperdine Univ, Pepperdine Graziadio Business Sch, Malibu, CA USA
[2] Chungnam Natl Univ, Dept Informat & Stat, Daejeon, South Korea
[3] Konkuk Univ, Dept Appl Stat, Seoul, South Korea
来源
R JOURNAL | 2020年 / 12卷 / 02期
基金
新加坡国家研究基金会;
关键词
TUNING PARAMETER SELECTION; CLIPPED ABSOLUTE DEVIATION; COORDINATE DESCENT; VARIABLE SELECTION; DIVERGING NUMBER; REGRESSION; REGULARIZATION; LIKELIHOOD; SHRINKAGE;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation and ridge regularization are provided also.
引用
收藏
页码:120 / 133
页数:14
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