Model selection with distributed SCAD penalty

Puyu Wang, Hai Zhang and Yong Liang

Journal of Applied Statistics, 2018, vol. 45, issue 11, 1938-1955

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2017.1401052 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:11:p:1938-1955

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2017.1401052

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().