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題名 基於Copula Entropy的變數選取方法與節點選取方法
Variable Selection Method and Knot Selection Method Based on Copula Entropy
作者 張軼棠
Chang, I-Tang
貢獻者 黃子銘
Huang, Tzee-Ming
張軼棠
Chang, I-Tang
關鍵詞 Lasso變數選取
Stepwise變數選取
Copula entropy
B-spline函數
Lasso Variable Selection
Stepwise Variable Selection
Copula Entropy
B-spline
日期 2024
上傳時間 5-Aug-2024 14:00:15 (UTC+8)
摘要 本研究透過copula entropy的應用,優化stepwise變數選取方法,並將其應用於B-spline函數中來近似實際函數,以挑選出必要的節點,同時又能減少與實際函數間的誤差。此方法利用copula entropy的獨特特性,以及RCV(refitted cross-validation)變異數估計方法,改善了stepwise變數選取的方法,此外,我們將改進方法與其他方法進行比較,以驗證其在實際應用中的效能表現。實驗結果顯示,此方法在回歸函數上誤差和準確率方面優於其他常見的變數選取方法,在近似spline函數上於部分情況中也表現出較佳的節點挑選效果,進而在B-spline函數的應用中實現更有效率的節點選擇。
In this study, we optimize the stepwise variable selection method through the application of copula entropy and apply it to the B-spline function to approximate the actual function in order to pick out the necessary knots and at the same time reduce the error with the actual function. This method improves the stepwise variable selection method by utilizing the unique characteristics of copula entropy and the RCV (refitted cross-validation) estimation method. In addition, we compare the improved method with other methods to verify its performance in practical applications. The experimental results show that this method outperforms other common variable selection methods in terms of error and accuracy on the regression function, and also shows better knot selection on the approximate spline function in some cases, which leads to more efficient knot selection in the application of the B-spline function.
參考文獻 Ty Adams. Forward selection via distance correlation. 2019. Jianqing Fan, Shaojun Guo, and Ning Hao. Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 74(1):37–65, 10 2011. Arthur E. Hoerl and Robert W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 42(1):80–86, 2000. Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. Estimating mutual information. Physical Review E, 69(6), June 2004. Jian Ma. Variable selection with copula entropy. ArXiv, abs/1910.12389, 2019. Jian Ma. copent: Estimating copula entropy and transfer entropy in r, 2021. E Sunandi, K A Notodoputro, and B Sartono. A study on group lasso for grouped variable selection in regression model. IOP Conference Series: Materials Science and Engineering, 1115(1):012089, 2021. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267–288, 1996. Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B, 68:49–67, 2006. Dennis D Boos Yujun Wu and Leonard A Stefanski. Controlling variable selection by the addition of pseudovariables. Journal of the American Statistical Association, 102(477):235–243, 2007. Hui Zou. The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476):1418–1429, 2006. Hui Zou and Trevor Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2):301–320, 2005.
描述 碩士
國立政治大學
統計學系
111354025
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111354025
資料類型 thesis
dc.contributor.advisor 黃子銘zh_TW
dc.contributor.advisor Huang, Tzee-Mingen_US
dc.contributor.author (Authors) 張軼棠zh_TW
dc.contributor.author (Authors) Chang, I-Tangen_US
dc.creator (作者) 張軼棠zh_TW
dc.creator (作者) Chang, I-Tangen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 14:00:15 (UTC+8)-
dc.date.available 5-Aug-2024 14:00:15 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 14:00:15 (UTC+8)-
dc.identifier (Other Identifiers) G0111354025en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152780-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 111354025zh_TW
dc.description.abstract (摘要) 本研究透過copula entropy的應用,優化stepwise變數選取方法,並將其應用於B-spline函數中來近似實際函數,以挑選出必要的節點,同時又能減少與實際函數間的誤差。此方法利用copula entropy的獨特特性,以及RCV(refitted cross-validation)變異數估計方法,改善了stepwise變數選取的方法,此外,我們將改進方法與其他方法進行比較,以驗證其在實際應用中的效能表現。實驗結果顯示,此方法在回歸函數上誤差和準確率方面優於其他常見的變數選取方法,在近似spline函數上於部分情況中也表現出較佳的節點挑選效果,進而在B-spline函數的應用中實現更有效率的節點選擇。zh_TW
dc.description.abstract (摘要) In this study, we optimize the stepwise variable selection method through the application of copula entropy and apply it to the B-spline function to approximate the actual function in order to pick out the necessary knots and at the same time reduce the error with the actual function. This method improves the stepwise variable selection method by utilizing the unique characteristics of copula entropy and the RCV (refitted cross-validation) estimation method. In addition, we compare the improved method with other methods to verify its performance in practical applications. The experimental results show that this method outperforms other common variable selection methods in terms of error and accuracy on the regression function, and also shows better knot selection on the approximate spline function in some cases, which leads to more efficient knot selection in the application of the B-spline function.en_US
dc.description.tableofcontents 1 緒論 7 2 文獻回顧與背景介紹 9 3 研究方法 12 3-1. 迴歸模型及兩種stepwise變數選取方法 12 3-1.1 Copula entropy 12 3-1.2 兩種修改後的stepwise變數選取方法 13 3-2. Spline迴歸近似模型 17 4 資料模擬分析 20 4-1. 評估指標 20 4-2. 資料生成與實驗過程 21 4-2.1 線性迴歸模型資料生成 21 4-2.2 無母數迴歸模擬資料生成 22 4-3. 實驗結果 22 5 結論與建議 33 5-1. 結論 33 5-2. 研究建議 34 參考文獻 35zh_TW
dc.format.extent 957142 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111354025en_US
dc.subject (關鍵詞) Lasso變數選取zh_TW
dc.subject (關鍵詞) Stepwise變數選取zh_TW
dc.subject (關鍵詞) Copula entropyzh_TW
dc.subject (關鍵詞) B-spline函數zh_TW
dc.subject (關鍵詞) Lasso Variable Selectionen_US
dc.subject (關鍵詞) Stepwise Variable Selectionen_US
dc.subject (關鍵詞) Copula Entropyen_US
dc.subject (關鍵詞) B-splineen_US
dc.title (題名) 基於Copula Entropy的變數選取方法與節點選取方法zh_TW
dc.title (題名) Variable Selection Method and Knot Selection Method Based on Copula Entropyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ty Adams. Forward selection via distance correlation. 2019. Jianqing Fan, Shaojun Guo, and Ning Hao. Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 74(1):37–65, 10 2011. Arthur E. Hoerl and Robert W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 42(1):80–86, 2000. Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. Estimating mutual information. Physical Review E, 69(6), June 2004. Jian Ma. Variable selection with copula entropy. ArXiv, abs/1910.12389, 2019. Jian Ma. copent: Estimating copula entropy and transfer entropy in r, 2021. E Sunandi, K A Notodoputro, and B Sartono. A study on group lasso for grouped variable selection in regression model. IOP Conference Series: Materials Science and Engineering, 1115(1):012089, 2021. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267–288, 1996. Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B, 68:49–67, 2006. Dennis D Boos Yujun Wu and Leonard A Stefanski. Controlling variable selection by the addition of pseudovariables. Journal of the American Statistical Association, 102(477):235–243, 2007. Hui Zou. The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476):1418–1429, 2006. Hui Zou and Trevor Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2):301–320, 2005.zh_TW