English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88613/118155 (75%)
Visitors : 23491534      Online Users : 619
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
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/110784
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/110784

    Title: B-Splines不同節點選擇方法之比較
    The comparison between different methods of knots selection for B-Splines
    Authors: 胡子卿
    Hu, Zi-Qing
    Contributors: 黃子銘
    Huang, Tzee-Ming
    Hu, Zi-Qing
    Keywords: 弧線函數
    Date: 2017
    Issue Date: 2017-07-11 11:26:14 (UTC+8)
    Abstract: 本文以 B-Spline 的框架研究比較兩種不同的節點估計方法。第一種方法是通過最優化特定 的目標函數並結合相對應的選擇標準選擇出最優化的節點組合。第二種方法則基於幾何控制多 邊形的特性將內部節點的選擇過程與幾何圖形聯繫起來,省去了最優化的過程。另外,本文採 用『節點估計時間』與『誤差平方和』(Mean Squared Error)來評價兩種方法的估計結果。通 過分析各種不同模擬數據下兩種方法的表現情況,本文的主要發現是:第一,無論哪種資料, 第二種方法在計算速度上都是大幅領先第一種方法。第二,在數據資料較小的情況下,第一種 方法中由 Lindstrom 提出的算法並不能很好的配飾模型,最後的估計誤差較大。而在數據資料 較多的情形下,誤差與其他方法較為接近。第三,第一種方法中沒有懲罰項的算法在所有驗證 過的數據中,其表現是所有方法中最穩定且估計誤差最小的。這些發現為如何選擇恰當的節點 估計方法提供了很具價值的參考信息
    This study compares two different methods of knot selection for B-Spline. The first one chooses the best knots through optimizing specific objective functions and corresponding crite- rion. Based on some properties of geometric control polygon, the second one connects the knot selection process with geometric figures, which avoids the tedious optimization. On the other hand, we use the time for estimation and the mean squared error to evaluate the performance of these two methods. There are three main findings of this study. The first finding is that the calculation speed of second method is much higher than that of the first one. Secondly, the algorithm proposed by Lindstrom in the first method is not stable and its estimation error is larger when the sample size is small. On the contrary, the performance of the algorithm proposed by Lindstrom becomes better as the sample size increases. Thirdly, the performance of the algorithm without penalty term in the first method is always better than the second method.
    Reference: 參考文獻
    [1] Gleb Beliakov. Cutting angle method–a tool for constrained global optimization. Opti- mization Methods and Software, 19(2):137–151, 2004.
    [2] Clemens Biller. Adaptive bayesian regression splines in semiparametric generalized linear models. Journal of Computational and Graphical Statistics, 9(1):122–140, 2000.
    [3] Hermann G Burchard. Splines (with optimal knots) are better. Applicable Analysis, 3(4):309–319, 1974.
    [4] Carl De Boor. A practical guide to splines; rev. ed. Applied mathematical sciences. Springer, Berlin, 2001.
    [5] DGT Denison, BK Mallick, and AFM Smith. Automatic bayesian curve fitting. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2):333–350, 1998.
    [6] Paul HC Eilers and Brian D Marx. Flexible smoothing with b-splines and penalties. Statistical science, pages 89–102, 1996.
    [7] Randall L Eubank. Spline smoothing and nonparametric regression. Marcel Dekker, 1988.
    [8] Jerome H Friedman. Multivariate adaptive regression splines. The annals of statistics, pages 1–67, 1991.
    [9] Jerome H Friedman and Bernard W Silverman. Flexible parsimonious smoothing and additive modeling. Technometrics, 31(1):3–21, 1989.
    [10] David LB Jupp. Approximation to data by splines with free knots. SIAM Journal on Numerical Analysis, 15(2):328–343, 1978.
    [11] Vladimir K Kaishev, Dimitrina S Dimitrova, Steven Haberman, and Richard J Ver- rall. Geometrically designed, variable knot regression splines. Computational Statistics, 31(3):1079–1105, 2016.
    [12] Hongmei Kang, Falai Chen, Yusheng Li, Jiansong Deng, and Zhouwang Yang. Knot calculation for spline fitting via sparse optimization. Computer-Aided Design, 58:179–188, 2015.
    [13] Charles Kooperberg, Charles J Stone, and Young K Truong. Hazard regression. Journal of the American Statistical Association, 90(429):78–94, 1995.
    [14] Thomas CM Lee. Regression spline smoothing using the minimum description length principle. Statistics & probability letters, 48(1):71–82, 2000.
    [15] Mary J Lindstrom. Penalized estimation of free-knot splines. Journal of Computational and Graphical Statistics, 8(2):333–352, 1999.
    [16] Satoshi Miyata and Xiaotong Shen. Free-knot splines and adaptive knot selection. Journal of the Japan Statistical Society, 35(2):303–324, 2005.
    [17] Nicolas Molinari, Jean-François Durand, and Robert Sabatier. Bounded optimal knots for regression splines. Computational statistics & data analysis, 45(2):159–178, 2004.
    [18] Finbarr O’sullivan, Brian S Yandell, and William J Raynor Jr. Automatic smoothing of regression functions in generalized linear models. Journal of the American Statistical Association, 81(393):96–103, 1986.
    [19] Daryl Pregibon. Logistic regression diagnostics. The Annals of Statistics, pages 705–724, 1981.
    [20] Carl Runge. Über empirische funktionen und die interpolation zwischen äquidistanten ordinaten. Zeitschrift für Mathematik und Physik, 46(224-243):20, 1901.
    [21] Larry Schumaker. Spline functions: basic theory. Cambridge University Press, 2007.
    [22] Michael Smith and Robert Kohn. Nonparametric regression using bayesian variable selec- tion. Journal of Econometrics, 75(2):317–343, 1996.
    [23] Michael Smith, Chi-Ming Wong, and Robert Kohn. Additive nonparametric regression with autocorrelated errors. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(2):311–331, 1998.
    [24] Charles J Stone, Mark H Hansen, Charles Kooperberg, Young K Truong, et al. Polynomial splines and their tensor products in extended linear modeling: 1994 wald memorial lecture. The Annals of Statistics, 25(4):1371–1470, 1997.
    [25] Wannes Van Loock, Goele Pipeleers, Joris De Schutter, and Jan Swevers. A convex opti- mization approach to curve fitting with b-splines. IFAC Proceedings Volumes, 44(1):2290– 2295, 2011.
    [26] Grace Wahba. A Survey of Some Smoothing Problems and the Method of the Generalized Cross-validation for Solving Them. University of Wisconsin, Department of Statistics, 1976.
    [27] Grace Wahba. Spline models for observational data. SIAM, 1990.
    [28] Yuan Yuan, Nan Chen, and Shiyu Zhou. Adaptive b-spline knot selection using multi- resolution basis set. IIE Transactions, 45(12):1263–1277, 2013.
    [29] Shanggang Zhou and Xiaotong Shen. Spatially adaptive regression splines and accurate knot selection schemes. Journal of the American Statistical Association, 96(453):247–259, 2001.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104354033
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    403301.pdf667KbAdobe PDF254View/Open

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

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback