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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/88359
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/88359


    Title: 改良式脊迴歸分析法於預測模式之應用
    Applied Improved Ridge Regression Analysis
    Authors: 周玫芳
    Chou, Mei Fang
    Contributors: 謝邦昌
    Shia, Ben Chang
    周玫芳
    Chou, Mei Fang
    Keywords: 脊迴歸
    刀削法
    偏量估計式
    Ridge Regression
    Jackknife
    Biased estimator
    Date: 1994
    1993
    Issue Date: 2016-04-29 15:31:04 (UTC+8)
    Abstract: 當我們在應用迴歸分析法時,往往會遇到兩個或多個自變數間存在著線性
    Reference: 1.林燦隆、謝邦昌、唐榮澤,(1989),利用氣象因子建立甘
    蔗糖份含量之預測模式,台灣糖業研究所研究彙報,
    124:1-12

    2.鄭敏祿,(1985),脊廻歸估計之模擬研究。國立政治大
    學統計學研究所碩士論文。

    3.謝邦昌,(1991),綜合預測模式之探討-甘蔗蔗產量及
    糖份含量依氣象因子二段式加權預測模式之研究。國立台
    灣大學農藝學研究所生物統計組博士論文。


    4. Brown, W.G. and Beattie, B.R. (1975).Improving Estimates Of Economic Parameters By Use Of Ridge Regression With Production Function Applications. Am.J.Agric.Econ.,57,21-32

    5. Douglas, G.F.(1978).Ridge Regression:When Biased Estimation Is Better, Social Science Ouarterly, Vol.58,No.4,March: 708-716

    6. Draper, N. R. and Smith , H. (1981). Applied Regression Analysis, Second Edition, New York.

    7.Ellen ,B.R.(1991). Prediction Error and Its Estimation for Subset-Selected Models, Technometrics, Vol.33, No.4,November:459-467

    8.Hinkley, D.V. (1977). Jackknifing In Unbalanced Situations, Technometrics, Vol.19,No.3,August:285-292

    9.Hoerl, A.E. and Kennard, R.W. (1970). Ridge Regression : Biased Estimation For Nonorthogonal Problems, Technometrics, Vol.12, No.1, February:55-67

    10. Hoerl, A.E. and Kennard, R.W. (1970). Ridge Regression : Applications to Nonorthogonal Problems , Technometrics, Vol.12, No.1, February:69-83

    11. Hoerl, A.E. Kennard, R.W. and Baldwin,K.F.(1975) Ridge Regression: Some Simulations, Communications In Statistics, 4(2), 105-123

    12.Hoerl,A.E. and Kennard,R.W.(1976).Ridge Regression : Iterative Estimation Of The Biased Parameter, Commun.Statis. Theor. Meth.A5(1):77-88

    13.John Neter, (1989). Applied Linear Regression Models, Second Edition, Boston.

    14.Loesgen,K.H.(1990). Generalization and Bayesian Interpretation of Ridge-Type Estimators with Good Prior Means, Statistical Papers, 31:147-157

    15.McDonald ,G.C. and Galarneau,D.I. (1975). A Monte Carlo evaluation of Some Ridge Type estimators.JASA,70:407-416

    16.Miller,R.G. (1968) Jackkinfing Variance. Ann.Math.Stqtist.,39:567-582

    17.Miller,R.G. (1974). An Unbalanced Jackknife, The Annals Of Statistics, Vol 2, No.5:880-891

    18.Nityananda Sarkar, (1992), A New Estimator Combining The Ridge Regression And The Restricted Least Squares Methods Of Estimation, Commun. Statist. Theory Meth.,21(7):1987-2000

    19.Nordberg, L. (1982). A Procedure For Determination Of A Good Ridge Parameter In Linear Regression. Commun. Statist. B11:285-309

    20.Quenouille,M.H.(1956).Note On Bias In Estimation. Biometrika. 43:353-360

    21.SAS/IML User’s Guide.(1985). SAS Institute Inc. North Carolina.
    22.Segerstedt,B.(1992).On Ordinary Ridge Regression In Generalized Linear Models, Commun.Statist.-Theory Meth., 21(8):2227-2246

    23.Shao,J. and Wu,C.F.J. (1987), Heterpscedastocoty-Robustness Of Jackknife Variance Estimators In Linear Models, The Annals Of Statistics, Vol.15 ,No. 4:1563-1579

    24.Shao,J.(1992).Jackknifing In Generalized Linear Models, Ann.Inst. Statist. Math. Vol.44,No. 4,673-686

    25. Turkey, J.W. (1958) Bias And Confidence In Not-Quite Large Samples. Ann.Math.Statist.,29:614
    Description: 碩士
    國立政治大學
    統計學系
    81354014
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002003827
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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