文章基本信息

标题：BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models
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作者：Canhong Wen ; Aijun Zhang ; Shijie Quan 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：94
期号：1
页码：1-24
DOI：10.18637/jss.v094.i04
出版社：University of California, Los Angeles
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.
关键词：best subset selection;primal dual active set;model selection;variable selection;R;C ;Rcpp.
其他关键词：best subset selection;primal dual active set;model selection;variable selection;R;C ;Rcpp