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題名 聚類分析在迴歸模型中選擇影響資料之研究
作者 林貞純
貢獻者 黃登源
林貞純
日期 1989
上傳時間 4-May-2016 14:24:12 (UTC+8)
參考文獻 [1] Andrews, D. F., and Pregibon, D. (1978): Finding the Outliers That Matter, Journal of the Royal Statistical Society, Ser. B, 40. pp.85-93.
     [2] Aroian, L. A. t1942): The Relationship of Fisher`s Z-Distribution to Student`s T Distribution (Abstract). Annals of Mathematical Statistics 13. PP. 451-452.
     [3] Belsley. D. A., Kuh. E., and Welsch. R. E. (1980): Regression Diagnostics: Identifying Influential Data and Sources of Collinearity New York:John Wiley.
     [4] Brownlee, K. A., (1965): Statistical Theory and Methodology in Science and Engineering, Wiley. (2nd ed.)
     [5] Cook, R. D ., (1977): Detection of Influential Observations in Linear Regression. Technometrics, 19, PP. 15-18.
     [6] Cook. R. D., (1979): Influential Observations in Linear Regression, Journal of the American Statistical Association, 74, pp. 169-174.
     [7] Cook. R. D., and Weisberg, s. (1980): Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression. Technometrics. 22. Pp. 495-508.
     [8] Cook, R. D., and Weisberg, S. (1982): Residuals and Influence in Regression, Hew York: Chapman and Hall. PP. 135.
     [9] Daniel, C., and Wood, F. S. (1980): Fitting Equations to Data, (2nd ed.) , New York: John Wiley. pp. 60-82.
     [10] Duda, R. 0., and Hart, P. E. (1973): Pattern Classification and Scene Analysis, New York: Wiley. PP. 239-243.
     [11] Ling, R. F. (1972): On the Theory and Construction of K-Clusters. Computer Journal. 15. PP. 326-332.
     [12] Ling. R. F. (1973): A Computer - Generated Aid for Cluster Analysis. Communications of the Association for Computing Machinery, 16. PP. 355-361.
     [13] Milligan, G. W., and Cooper. H. C. (1985): An Examination of Procedures for Determining the Humber of Cluster in A Data Set. Psychometrika. 50. PP. 159-179.
     [14] Welsch. R. E. (1982): Influence Functions and Regression Diagnostics, in Modern Data Analysis. eds. R. Launer and A. Siegel. New York: Academic Press.
描述 碩士
國立政治大學
統計學系
資料來源 http://thesis.lib.nccu.edu.tw/record/#B2002005731
資料類型 thesis
dc.contributor.advisor 黃登源zh_TW
dc.contributor.author (Authors) 林貞純zh_TW
dc.creator (作者) 林貞純zh_TW
dc.date (日期) 1989en_US
dc.date.accessioned 4-May-2016 14:24:12 (UTC+8)-
dc.date.available 4-May-2016 14:24:12 (UTC+8)-
dc.date.issued (上傳時間) 4-May-2016 14:24:12 (UTC+8)-
dc.identifier (Other Identifiers) B2002005731en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/90497-
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.tableofcontents 目錄
     第一章 緒論……1
     第一節 研究動機與目的……1
     第二節 本文結構……3
     第二章 迴歸模型中辨認影響資料之基本理論……5
     第一節 迴歸模型中影響資料的定義……5
     第二節 辨認影響資料組之導引……8
     第三章 帽子矩陣(hat matrix)與影響測度量的基本觀念……10
     第一節 帽子矩陣(H)的應用……10
     第二節 兩種影響測度量的介紹……13
     第三節 修正的帽子矩陣(H*)之應用……21
     註解……28
     第四章 聚類分析在迴歸模型中選擇影響資料組之理論……45
     第一節 K聚類法與單一連鎖法……45
     第二節 H*矩陣的聚類演算與黑色陰影圖……48
     第五章 影響資料組之決定……50
      註解……54
     第六章 舉例……56
     第一節 前言……56
     第二節 資料來源與描述
     第三節 分析結果提報與討論……58
     第七章 結論……68
     附錄……69
     參考文獻……87		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002005731en_US
dc.title (題名) 聚類分析在迴歸模型中選擇影響資料之研究zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Andrews, D. F., and Pregibon, D. (1978): Finding the Outliers That Matter, Journal of the Royal Statistical Society, Ser. B, 40. pp.85-93.
     [2] Aroian, L. A. t1942): The Relationship of Fisher`s Z-Distribution to Student`s T Distribution (Abstract). Annals of Mathematical Statistics 13. PP. 451-452.
     [3] Belsley. D. A., Kuh. E., and Welsch. R. E. (1980): Regression Diagnostics: Identifying Influential Data and Sources of Collinearity New York:John Wiley.
     [4] Brownlee, K. A., (1965): Statistical Theory and Methodology in Science and Engineering, Wiley. (2nd ed.)
     [5] Cook, R. D ., (1977): Detection of Influential Observations in Linear Regression. Technometrics, 19, PP. 15-18.
     [6] Cook. R. D., (1979): Influential Observations in Linear Regression, Journal of the American Statistical Association, 74, pp. 169-174.
     [7] Cook. R. D., and Weisberg, s. (1980): Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression. Technometrics. 22. Pp. 495-508.
     [8] Cook, R. D., and Weisberg, S. (1982): Residuals and Influence in Regression, Hew York: Chapman and Hall. PP. 135.
     [9] Daniel, C., and Wood, F. S. (1980): Fitting Equations to Data, (2nd ed.) , New York: John Wiley. pp. 60-82.
     [10] Duda, R. 0., and Hart, P. E. (1973): Pattern Classification and Scene Analysis, New York: Wiley. PP. 239-243.
     [11] Ling, R. F. (1972): On the Theory and Construction of K-Clusters. Computer Journal. 15. PP. 326-332.
     [12] Ling. R. F. (1973): A Computer - Generated Aid for Cluster Analysis. Communications of the Association for Computing Machinery, 16. PP. 355-361.
     [13] Milligan, G. W., and Cooper. H. C. (1985): An Examination of Procedures for Determining the Humber of Cluster in A Data Set. Psychometrika. 50. PP. 159-179.
     [14] Welsch. R. E. (1982): Influence Functions and Regression Diagnostics, in Modern Data Analysis. eds. R. Launer and A. Siegel. New York: Academic Press.		zh_TW