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

    Bibliographical note

    Publisher Copyright:
    © 2018 IEEE.

    ASJC Scopus subject areas

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

    Fingerprint

    Dive into the research topics of 'A Comparison of Penalized Regressions for Estimating Directed Acyclic Networks'. Together they form a unique fingerprint.

    Cite this