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題名 基於LASSO和FORWARD的節點選取方法比較
A comparison between two knot selection methods based on LASSO and FORWARD selection
作者 孟耿德
Meng, Geng De
貢獻者 黃子銘
Huang, Tzee Ming
孟耿德
Meng, Geng De
關鍵詞 變數選取
最小壓縮法
KNOT
LASSO
日期 2017
上傳時間 13-Sep-2017 14:12:10 (UTC+8)
摘要 在無母數迴歸問題中,如果迴歸函數以spline函數近似,而且使用等距節點,則節點選取可以視為一個變數選取的問題。TiBshirani(1996)提出最小絕對壓縮挑選運算(Least Absolute Shrinkage and Selection Operator; LASSO)能夠對變數縮減,本研究中將考慮使用LASSO和forward 兩種選取變數方法進行節點選取。根據本研究模擬結果,forward選取方法的挑選節點效果比較好。
In nonparametric regression, if the regression function is approximated using a spline function with equally spaced knots ,then the problem of knot selection can Be considered as a variable selection problem. Tibshirani(1996) proposed Least Absolute Shrinkage and Selection Operator(LASSO), which can Be used for variable selection. In this thesis, two variable selection methods: LASSO and forward, are considered for knots selection. According to the simulation results in this thesis, the forward method is better for knot selection.
參考文獻 參考文獻
     
     [1]Charles J. Stone(1997)Polynomial Splines and their Tensor Products in Extended Linear Modeling;p1374-p1377
     [2]Denison, D., Mallick, B., and Smith, A. (1998). Automatic Bayesian curve fitting, J. R. Statist. Soc., B, 60, 333–350
     [3]EuBank, R.L. (1988). Smoothing Splines and Non-parametric Regression, Marcel Dekker, New Yorkand Base
     [4 ]Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal proBlems. Technometrics 12, 55-67.
     [5]I. J. SchoenBerg, On trigonometric spline interpolation, J. Math. Mech. 13(1964), 795-825
     [6]Michael R. OsBorne, Brett Presnell, and Berwin A. Turlach. Knot selection for regression splines via the LASSO. In Computing Science and Statistics. Dimen-sion Reduction, Computational Complexity and Information. Proceedings of the 30th Symposium on the Interface, pages 44–49, 1998
     [7]WahBa, G. (1990) Spline Models for OBservational Data.
     [8] R. TiBshirani. Regression shrinkage and selection via the LASSO. Journal of the RoyalStatistical Society (Series B), 58:267–288, 1996.
     [9 ] Schumaker, L. L. (1981) Spline functions, Wiley, New York.
描述 碩士
國立政治大學
統計學系
104354029
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1043540291
資料類型 thesis
dc.contributor.advisor 黃子銘zh_TW
dc.contributor.advisor Huang, Tzee Mingen_US
dc.contributor.author (Authors) 孟耿德zh_TW
dc.contributor.author (Authors) Meng, Geng Deen_US
dc.creator (作者) 孟耿德zh_TW
dc.creator (作者) Meng, Geng Deen_US
dc.date (日期) 2017en_US
dc.date.accessioned 13-Sep-2017 14:12:10 (UTC+8)-
dc.date.available 13-Sep-2017 14:12:10 (UTC+8)-
dc.date.issued (上傳時間) 13-Sep-2017 14:12:10 (UTC+8)-
dc.identifier (Other Identifiers) G1043540291en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112618-
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) 104354029zh_TW
dc.description.abstract (摘要) 在無母數迴歸問題中,如果迴歸函數以spline函數近似,而且使用等距節點,則節點選取可以視為一個變數選取的問題。TiBshirani(1996)提出最小絕對壓縮挑選運算(Least Absolute Shrinkage and Selection Operator; LASSO)能夠對變數縮減,本研究中將考慮使用LASSO和forward 兩種選取變數方法進行節點選取。根據本研究模擬結果,forward選取方法的挑選節點效果比較好。zh_TW
dc.description.abstract (摘要) In nonparametric regression, if the regression function is approximated using a spline function with equally spaced knots ,then the problem of knot selection can Be considered as a variable selection problem. Tibshirani(1996) proposed Least Absolute Shrinkage and Selection Operator(LASSO), which can Be used for variable selection. In this thesis, two variable selection methods: LASSO and forward, are considered for knots selection. According to the simulation results in this thesis, the forward method is better for knot selection.en_US
dc.description.tableofcontents 第一章 緒論 1
     第二章 文獻迴顧 3
     第三章 研究方法 4
     第一節 模型假設與節點對應變數關係 4
     第二節 LASSO運算 5
     第四章 模擬和比較 7
      第一節 節點設定 7
      第二節 模擬比較 10
     第五章 結論與建議 11
zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1043540291en_US
dc.subject (關鍵詞) 變數選取zh_TW
dc.subject (關鍵詞) 最小壓縮法zh_TW
dc.subject (關鍵詞) KNOTen_US
dc.subject (關鍵詞) LASSOen_US
dc.title (題名) 基於LASSO和FORWARD的節點選取方法比較zh_TW
dc.title (題名) A comparison between two knot selection methods based on LASSO and FORWARD selectionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 參考文獻
     
     [1]Charles J. Stone(1997)Polynomial Splines and their Tensor Products in Extended Linear Modeling;p1374-p1377
     [2]Denison, D., Mallick, B., and Smith, A. (1998). Automatic Bayesian curve fitting, J. R. Statist. Soc., B, 60, 333–350
     [3]EuBank, R.L. (1988). Smoothing Splines and Non-parametric Regression, Marcel Dekker, New Yorkand Base
     [4 ]Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal proBlems. Technometrics 12, 55-67.
     [5]I. J. SchoenBerg, On trigonometric spline interpolation, J. Math. Mech. 13(1964), 795-825
     [6]Michael R. OsBorne, Brett Presnell, and Berwin A. Turlach. Knot selection for regression splines via the LASSO. In Computing Science and Statistics. Dimen-sion Reduction, Computational Complexity and Information. Proceedings of the 30th Symposium on the Interface, pages 44–49, 1998
     [7]WahBa, G. (1990) Spline Models for OBservational Data.
     [8] R. TiBshirani. Regression shrinkage and selection via the LASSO. Journal of the RoyalStatistical Society (Series B), 58:267–288, 1996.
     [9 ] Schumaker, L. L. (1981) Spline functions, Wiley, New York.
zh_TW