Variable Selection with Nonconcave Penalty Function on Reduced-Rank Regression
作者: Sang Yong Jung Chongsun Park
刊名: Communications for Statistical Applications and Methods, 2015, Vol.22 (1)
来源数据库: Communications for Statistical Applications and Methods
DOI: 10.5351/CSAM.2015.22.1.041
关键词: Group penaltymultivariate linear modelnonconcave penaltyreduced-rank regressionvariable selection.
原始语种摘要: In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using L1 penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of...
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  • selection 选择
  • penalty 罚款
  • variable 变量
  • estimate 估计
  • deviation 偏差
  • bounded 有界的
  • competitive 竞争性的
  • relevant 有关联的
  • simultaneously 同时地
  • Selection 分选