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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/140833


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140833


    Title: Unraveling Hidden Major Factors by Breaking Heterogeneity into Homogeneous Parts within Many-System Problems
    Authors: 周珮婷
    Chou, Elizabeth P.
    Chen, Ting-Li;Fushing, Hsieh
    Contributors: 統計系
    Keywords: CEDA;Magnus effect;conditional entropy;heterogeneity;mutual information;Rosenberg Self-Esteem Scale
    Date: 2022-01
    Issue Date: 2022-07-07 11:24:14 (UTC+8)
    Abstract: For a large ensemble of complex systems, a Many-System Problem (MSP) studies how heterogeneity constrains and hides structural mechanisms, and how to uncover and reveal hidden major factors from homogeneous parts. All member systems in an MSP share common governing principles of dynamics, but differ in idiosyncratic characteristics. A typical dynamic is found underlying response features with respect to covariate features of quantitative or qualitative data types. Neither all-system-as-one-whole nor individual system-specific functional structures are assumed in such response-vs-covariate (Re–Co) dynamics. We developed a computational protocol for identifying various collections of major factors of various orders underlying Re–Co dynamics. We first demonstrate the immanent effects of heterogeneity among member systems, which constrain compositions of major factors and even hide essential ones. Secondly, we show that fuller collections of major factors are discovered by breaking heterogeneity into many homogeneous parts. This process further realizes Anderson’s “More is Different” phenomenon. We employ the categorical nature of all features and develop a Categorical Exploratory Data Analysis (CEDA)-based major factor selection protocol. Information theoretical measurements—conditional mutual information and entropy—are heavily used in two selection criteria: C1—confirmable and C2—irreplaceable. All conditional entropies are evaluated through contingency tables with algorithmically computed reliability against the finite sample phenomenon. We study one artificially designed MSP and then two real collectives of Major League Baseball (MLB) pitching dynamics with 62 slider pitchers and 199 fastball pitchers, respectively. Finally, our MSP data analyzing techniques are applied to resolve a scientific issue related to the Rosenberg Self-Esteem Scale.
    Relation: Entropy, Vol.24, No.2, 170
    Data Type: article
    DOI 連結: https://doi.org/10.3390/e24020170
    DOI: 10.3390/e24020170
    Appears in Collections:[統計學系] 期刊論文

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