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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/129647
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/129647

    Title: 基於 multi-resolution B-spline basis 之二維曲面估計
    Estimate of the two-dimensions surface based on multi-resolution B-spline basis
    Authors: 林哲宇
    Lin, Zhe-Yu
    Contributors: 黃子銘
    Lin, Zhe-Yu
    Keywords: B-spline迴歸
    B-spline regression
    Knot selection
    Surface estimation
    Date: 2020
    Issue Date: 2020-05-05 11:56:46 (UTC+8)
    Abstract: 本研究是根據Yuan[12]提出的Multi-resolution B-spline basis節點放置方法對於節點的篩選做進一步的改良,以擾動項ε的變異數σ^2為標準,採用向後刪除的概念提出方法一及方法二,也將其改良後的方法透過張量積擴展到二維曲面的估計,以copula密度函數作為迴歸函數,與核迴歸中的局部線性迴歸的結果進行比較。 依模擬結果,方法一會因為σ的估計膨脹而篩選掉過多節點,方法二受σ的估計影響較小,估計效果較佳,也較為穩定。 在以copula密度函數作為迴歸函數的模擬實驗中,較不平滑的迴歸函數使用方法二來估計,估計效果較佳;較平滑的迴歸函數使用局部線性迴歸來估計為最佳,但如果在σ的估計上能更好,方法二的估計效果可能優於局部線性迴歸。
    This thesis is based on the multi-resolution B-spline basis knots placement method proposed by Yuan[12] to further improve the selection of knots. Based on the variation of the disturbance term, Method 1 and Method 2,are proposed using the concept of backward deletion. The improved method is also extended to the estimation of the two-dimensional surface. through the tensor product. Simulation studies have been carried out to compare the performance of Methods 1 and 2, and local linear regression. According to the results of simulation studies, Method 1 tends to filter out too many knots because of the large estimation error of σ,and Method 2 is less affected by the estimation of σ. The estimation based on Method 2 is more accurate and stable. In the studies where Methods 1 and 2, and local linear regression are compared, Method 2 outperforms local linear regression when the regression function is less smooth. When the regression function is smooth, local linear regression performs better than Method 2. However,if the estimation of σ can be better, the estimation accuracy of Method 2 may be better than local linear regression.
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    Knot calculation for spline fitting via sparse optimization. Computer-Aided Design, 58:179–188, 2015.

    [6] Mary J. Lindstrom. Penalized estimation of free-knot splines. Journal of Computational and Graphical Statistics, 8(2):333–352, 1999.

    [7] Satoshi Miyata and Xiaotong Shen. Adaptive free-knot splines. Journal of Computational and Graphical Statistics, 12(1):197–213, 2003.

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    [9] Abe Sklar. Fonctions de r´epartition `a n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Universit´e de Paris, 8:229–231, 1959.

    [10] R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society (Series B), 58:267–288, 1996.

    [11] Wannes Van Loock, Goele Pipeleers, J. Schutter, and Jan Swevers. A convex optimization approach to curve fitting with b-splines. IFAC Proceedings Volumes (IFAC-PapersOnline), 18:2290–2295, 2011.

    [12] Yuan Yuan, Nan Chen, and Shiyu Zhou. Adaptive b-spline knot selection using multi-resolution basis set. IIE Transactions, 45:1263–1277, 2013.

    [13] 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/#G0106354016
    Data Type: thesis
    DOI: 10.6814/NCCU202000415
    Appears in Collections:[統計學系] 學位論文

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