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題名 Categorical Nature of Major Factor Selection via Information Theoretic Measurements
作者 周珮婷
Chou, Elizabeth P.
Chen, Ting-Li;Fushing, Hsieh
貢獻者 統計系
關鍵詞 CEDA; conditional entropy; conditional mutual information; heterogeneity; information gain
日期 2021.12
上傳時間 7-七月-2022 11:24:01 (UTC+8)
摘要 Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener–Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member’s major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.
關聯 Entropy, Vol.23, No.12, 1684
資料類型 article
DOI https://doi.org/10.3390/e23121684
dc.contributor 統計系
dc.creator (作者) 周珮婷
dc.creator (作者) Chou, Elizabeth P.
dc.creator (作者) Chen, Ting-Li;Fushing, Hsieh
dc.date (日期) 2021.12
dc.date.accessioned 7-七月-2022 11:24:01 (UTC+8)-
dc.date.available 7-七月-2022 11:24:01 (UTC+8)-
dc.date.issued (上傳時間) 7-七月-2022 11:24:01 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140831-
dc.description.abstract (摘要) Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener–Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member’s major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.
dc.format.extent 97 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Entropy, Vol.23, No.12, 1684
dc.subject (關鍵詞) CEDA; conditional entropy; conditional mutual information; heterogeneity; information gain
dc.title (題名) Categorical Nature of Major Factor Selection via Information Theoretic Measurements
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3390/e23121684
dc.doi.uri (DOI) https://doi.org/10.3390/e23121684