array(2) { ["lab"]=> string(4) "1409" ["publication"]=> string(5) "12607" } Model selection with distributed SCAD penalty - Liang Yong | LabXing

Liang Yong

简介 中藥質量控制資訊系統，生物資訊學

大学 Macau University of Science and Technology / Faculty of Information Technology
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Model selection with distributed SCAD penalty

2018
期刊 Journal of Applied Statistics
In this paper, we focus on the feature extraction and variable selection of massive data which is divided and stored in different linked computers. Specifically, we study the distributed model selection with the Smoothly Clipped Absolute Deviation (SCAD) penalty. Based on the Alternating Direction Method of Multipliers (ADMM) algorithm, we propose distributed SCAD algorithm and prove its convergence. The results of variable selection of the distributed approach are same with the results of the non-distributed approach. Numerical studies show that our method is both effective and efficient which performs well in distributed data analysis.

  • 卷 45
  • 期 11
  • 页码 1938-1955
  • Taylor & Francis
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