A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks

Kyu Min Lee, Sung Won Han, Hyungbin Yun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Network models can be classified into two large groups: undirected and directed. Directed network graphs that can represent causal relationships are likely more appropriate in bio-medical data. There have been many studies to estimate DAGs(Directed Acyclic Graphs), of which the two-stage approach using lasso effectively. Find the edges between the nodes in the first step and find the direction in the second step. In this paper, we try to compare which penalized regression is better to find neighborhoods through simulations. We present the result of the simulations that shows which penalized regression is the best.

Original languageEnglish
Title of host publicationICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages18-21
Number of pages4
ISBN (Print)9781538646465
DOIs
Publication statusPublished - 2018 Aug 14
Event10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic
Duration: 2018 Jul 32018 Jul 6

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2018-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Other

Other10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/TerritoryCzech Republic
CityPrague
Period18/7/318/7/6

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Cite this