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論文名稱 Title |
加權多項式迴歸模型之Ds最適設計 Ds-optimal designs for weighted polynomial regression |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
24 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2007-05-24 |
繳交日期 Date of Submission |
2007-06-21 |
關鍵字 Keywords |
泰勒展開式、加權多項式迴歸、遞迴演算法、隱函式定理、Ds-等價定理、柴比雪夫多項式、Ds-最適設計 weighted polynomial regression, recursive algorithm, Taylor expansion, Implicit Function Theorem, Ds-Equivalence Theorem, Ds-optimal design, Chebyshev polynomial |
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統計 Statistics |
本論文已被瀏覽 5955 次,被下載 2219 次 The thesis/dissertation has been browsed 5955 times, has been downloaded 2219 times. |
中文摘要 |
這篇文章主要是在研究區間[m-a,m+a],m, a in R,且具有可解析加權函式d次多項式迴歸的Ds-最適設計問題。當a趨近於0時,此時最適設計的結構僅決於d, a和加權函式。此外,可藉由一個遞迴公式來計算伸縮後的最適設計點和權重的泰勒多項式。 |
Abstract |
This paper is devoted to studying the problem of constructing Ds-optimal design for d-th degree polynomial regression with analytic weight function on the interval [m-a,m+a],m,a in R. It is demonstrated that the structure of the optimal design depends on d, a and weight function only, as a close to 0. Moreover, the Taylor polynomials of the scaled versions of the optimal support points and weights can be computed via a recursive formula. |
目次 Table of Contents |
Contents Abstract......................................................................ii 1 Introduction ...........................................................1 2 Preliminary .......................................................... 2 3 Taylor expansions for Ds-optimal designs ...7 4 Examples .............................................................8 5 Conclusions ........................................................14 Appendix ..................................................................14 References .............................................................17 |
參考文獻 References |
References Antille, G., Dette, H. and Weinberg, A. (2003). A note on optimal designs in weighted polynomial regression for the classical e±ciency functions. J. Statist. Plann. Inference 113, 285-292. Chang, F.-C. and Heiligers, B. (1995). E-optimal designs for polynomial regression without intercept. J. Statist. Plann. Inference 55, 371-387. Chang, F.-C. and Lin, G.-C. (1997). D-optimal designs for weighted polynomial regression. J. Statist. Plann. Inference 62, 317-331. Chang, F.-C. (2005). D-optimal designs for weighted polynomial regression|A functional-algebraic approach. Statist. Sinica 15, 153-163. Dette, H., Haines, L.M. and Imhof, L. (1999). Optimal designs for rational models and weighted polynomial regression. Ann. Statist. 27, 1272-1293. Dette, H., Melas, V.B. and Pepelyshev, A. (2004). Optimal designs for estimating individual coe±cients in polynomial regression{a functional approach. J. Statist. Plann. Inference 118, 201-219. Fedorov, V.V. (1972). Theory of Optimal Experiments. Translated and edited by Studden, W.J. and Klimko, E.M. Academic Press, New York. Graybill, F.A. (1983). Matrices with Applications in Statistics. Wadsworth, Belmont, CA. Hoel, P.G. (1958). Effciency problems in polynomial estimation. Ann. Math. Statist. 29, 1134-1145. Huang, M.-N.L., Chang, F.-C. and Wong, W.-K. (1995). D-optimal designs for polynomial regression without an intercept. Statist. Sinica 5, 441-458. Imhof, L., Kra®t, O. and Schaefer, M. (1998). D-optimal designs for polynomial regression with weight function x/(1 + x). Statist. Sinica 8, 1271-1274. Karlin, S. and Studden, W.J. (1966a). Optimal experimental designs. Ann. Math. Statist. 37, 783-815. Karlin, S. and Studden, W.J. (1966b). Tchebyche® System: With Applications in Analysis and Statistics. Wiley, New York. Kiefer, J.C. and Wolfowitz, J. (1959). Optimum designs in regression problems. Ann. Math. Statist. 30, 271-294. Kiefer, J. (1961). Optimum designs in regression problems, II. Ann. Math. Statist. 32, 298-325. Khuri, A.I. (2003). Advanced Calculus with Applications in Statistics, 2nd edition. Wiley, New York. Melas, V.B. (1978). Optimal designs for exponential regression. Math. Oper. Forsch. Statist. Ser. Statist. 9, 45-59. Melas, V.B. (2000). Analytic theory of E-optimal designs for polynomial regression. In: Balakrishnan, N. (Ed.), Advances in Stochastic Simulation Methods, 85-116. Springer Verlag, New York. Melas, V.B. (2001). Analytical properties of locally D-optimal designs for rational models. In: Atkinson, A.C., Hackl, P. and MÄuller, W.G. (Eds.) MODA 6|Advances in model-oriented design and analysis, 201-209. Physica-Verlag, New York. Melas, V.B. (2005). Functional Approach to Optimal Experimental Design. Springer Verlag, New York. Montgomery, D.C., Peck, E.A. and Vining, G.G. (2001). Introduction to Linear Regression Analysis, 3rd edition. Wiley, New York. Pukelsheim, F. (1993). Optimal Design of Experiments. Wiley, New York. Silvey, S.D. (1980). Optimal Design. Chapman & Hall, London. Studden, W.J. (1980). Ds-optimal designs for polynomial regression using continued fractions. Ann. Statist. 8, 1132-1141. Wolfram, S. (2003). The Mathematica Book, 5th edition. Wolfram Media, Champaign, IL. |
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