政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150386
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51584820      Online Users : 806
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150386


    Title: Multiscale major factor selections for complex system data with structural dependency and heterogeneity
    Authors: 周珮婷
    Chou, Elizabeth P.;Fushing, Hsieh;Chen, Ting-Li
    Contributors: 統計系
    Keywords: Broken symmetry;Conditional entropy;Contingency table;Major factor selection;Multiclass Classification;Pitching dynamics
    Date: 2023-11
    Issue Date: 2024-03-05 16:16:32 (UTC+8)
    Abstract: The unknown multiscale structure hidden in large complex systems is explored bottom-up through discovered heterogeneity under structural dependency embedded within structured data sets. Via two real complex systems, we demonstrate computed hierarchical structures with broken symmetry constituting data’s information content. Through graphic displays, such information content indirectly, but efficiently resolves system-related scientific issues that are difficult to resolve directly. All bottom-up explorations and computations are based on conditional entropy and mutual information evaluated upon contingency table platforms after categorizing all quantitative features. Categorical Exploratory Data Analysis (CEDA) first extracts global major factors that share significant mutual information with the targeted response (Re) variable against many covariate (Co) features under the presence of structural dependency. Then each global major factor is taken as one perspective of heterogeneity to subdivide the entire data set according to its categories into sub-collections. This simple “de-associating” protocol significantly reduces structural dependency among the rest of the features such that another run of major factor selection performed on the sub-collection scale can precisely identify which feature sets could provide extra information beyond the global major factor. Finally, informative patterns collected from multiple perspectives of heterogeneity are displayed to explicitly resolve issues of prediction, classification, and detecting minute dynamic changes.
    Relation: Physica A: Statistical Mechanics and its Applications, Vol.630, 129227
    Data Type: article
    DOI link: https://doi.org/10.1016/j.physa.2023.129227
    DOI: 10.1016/j.physa.2023.129227
    Appears in Collections:[Department of Statistics] Periodical Articles

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML97View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback