BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

被引:22
|
作者
Wen, Canhong [1 ,2 ]
Zhang, Aijun [3 ]
Quan, Shijie [2 ]
Wang, Xueqin [1 ,2 ]
机构
[1] Univ Sci & Technol China, Dept Stat & Finance, Sch Management, Hefei 230026, Anhui, Peoples R China
[2] Sun Yat Sen Univ, Dept Stat, Sch Math, Southern China Ctr Stat Sci, Guangzhou 510275, Guangdong, Peoples R China
[3] Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong, Peoples R China
来源
JOURNAL OF STATISTICAL SOFTWARE | 2020年 / 94卷 / 04期
基金
对外科技合作项目(国际科技项目);
关键词
best subset selection; primal dual active set; model selection; variable selection; R; C plus; Rcpp; REGULARIZATION PATHS;
D O I
10.18637/jss.v094.i04
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports sequential and golden search strategies for best subset selection. We provide a C++ implementation of the algorithm using an Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has competitive performance compared to other R packages for best subset selection purposes.
引用
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页码:1 / 24
页数:24
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