A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks

Kyu Min Lee; Sung Won Han; Hyungbin Yun

doi:10.1109/ICUFN.2018.8437027

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 proceeding › Conference 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 language	English
Title of host publication	ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	18-21
Number of pages	4
ISBN (Print)	9781538646465
DOIs	https://doi.org/10.1109/ICUFN.2018.8437027
Publication status	Published - 2018 Aug 14
Event	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic Duration: 2018 Jul 3 → 2018 Jul 6

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2018-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Other

Other	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/Territory	Czech Republic
City	Prague
Period	18/7/3 → 18/7/6

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

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10.1109/ICUFN.2018.8437027

Cite this

Lee, K. M., Han, S. W., & Yun, H. (2018). A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks. In ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks (pp. 18-21). [8437027] (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/ICUFN.2018.8437027

A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks. / Lee, Kyu Min; Han, Sung Won; Yun, Hyungbin.
ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2018. p. 18-21 8437027 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, KM, Han, SW & Yun, H 2018, A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks. in ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks., 8437027, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2018-July, IEEE Computer Society, pp. 18-21, 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018, Prague, Czech Republic, 18/7/3. https://doi.org/10.1109/ICUFN.2018.8437027

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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.",

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A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks

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