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題名 主成份分析在各種多元統計方法上的應用
作者 胡忠琳
貢獻者 葉小蓁
胡忠琳
日期 1989
上傳時間 4-五月-2016 14:23:56 (UTC+8)
參考文獻 參考文獻
     一 、中文部分
     1 沈伊藤:主成份分析與其他統計分析之比較研究，台北:國立政治大學統計研究所碩士論文，民國七十五年。
     2 林清山:多變量分析統計法，台北:台灣東華書局，民國七十三年。
     3 黃俊英:多變量分析，三版，台北:中華經濟企業研究所 華泰經銷，民國七十七年。
     4 楊浩二. .多變量統計方法，台北:華泰書局，民國七三年。
     5 楊浩二:多變量常態檢定及應用之研究，台北:華泰書局民國七十一年。
     6 閩建蜀，游漢民：市場研究：基本方法，台北：巨浪出版社，民國七十五年。
     
     二、英文部分
     1 Anderberg. M.R. (1973) : Cluster Analysis for Applications. New York: Academic Press. pp. 185-190.
     2 Anderson. T.W. (1963) : Asymptotic theory for principal component analysis . Annals of Mathematical Statistics. Vol 34, PP. 122-148.
     3 Anderson. T.W. (1984) : An Introduction to Multivariate Statistical Analysis. New York: John Wiley and Son.
     4 Bartlett. M. S. (950) : Tests of significance in factor analysis. Brit. J. Psychol. Statist. Section. Vol 3. PP. 77
     5 Bartlett, M.S. (1951) : discriminant analysis. Vol 22 . pp.107-111. An inverse matrix adjustment in Annals of Mathematical Statistics.
     6 Berenson. H.L. Levine. D.H. and Goldstein (983): Intermediate Statistical Methods and Application A Computer Package Approach. Prentice - Hall. Inc .,Englewood Chiffs.
     New Jersey. pp.458-462.
     7 Cattell .R.B. (978): The Scientific Use of Factor Analysis in Beharioral and Life Sciences. New York: Plenum Press pP.15-39.
     8 Chang. W.C. (979) : Confidence interval estimation and transformation of data in a mixture of two multivariate normal distributions with any given large dimension. Technometrics,
     Vol 21 . PP. 351-355.
     9 Chang. W.C. (1983) : On using principal components before separating a mixture of two multivariate normal distributions. Applied Statistics. Vol 32, pp.267-275.
     10 Cravens. D.W., Woodruff. R.B. and Stamper. J.C. (1972) : An analytical approach for evaluating sales territory performance. Journal of Marketing. pp.31-37.
     11 Daling. J.R. and Tamura. H. (1970) Use of orthogonal factors for selection of variables in a regression equation an illustration. Applied Statistics. Vol 19. pp.260-268.
     12 Day. ICE. (969): Estimating the components of a mixture of normal distributions. Biometrika. Vol 56, pp.463-474.
     13 Draper. N.R.. and Smith. H. (981) :Analysis. 2nd ed. New York: John pp. 294-325. Applied Regression Wiley and Son Inc .,pp.294-325
     14 Dunham. R.B. (1977): Reaction to job characteristics: moderating effects of the origanization. Academy of Management Journal. Vol 20 .no. 1 . pp.42-65.
     15 Eastment. H.T.. and Krzanowski. W.J. (1982) Cross-Validatory choice of the number of components from a principal component analysis. Technometrics, Vol 24 ,pp.73-77
     16 Everitt. B.S. (1980) : Cluster Analysis. 2nd ed London Heinemann Educational Books.
     17 Filliben, J.J. (1975) The probability plot correlation coefficient test for normality. Technometrics. Vol 17. no. 1,pp.1l1-117.
     18 Finn. J.D. (1974): A General Model for Multivariate Analysis. New York Holt Rinehart and Winston. pp.182-204.
     19 Gabriel, K.R. (1971): The biplot graphic display of matrices by additive and multiplicative models. Journal of Royal Statistical Society B. Vol 40 . pp.186-196.
     20 Gnanadesikan. R. and Kettenring. J.R. (1972) Robust es – timates, residuals. and outlier detection with wultiresponse data. Biometrics. vol 28. pp.81-124.
     21 Goodnight. J. and Wallace. T.D. (1972) : Operational techniques and tables for making weak MSE tests for restrications in regression. Econometrica. Vol 40 . pp.699-709.
     22 Gordon. A.D. (1981) Classification Method for the Exploratory Analysis of Multivariate Data. London : Chapman and Hall.
     23 Gower. J.C. (1966) : Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika .Vol 53. pp.325-338 .
     24 Gunst. R. F. and Mason. R. L. (1980): Regress i on Analysis and Its Application: Data-Oriented Approach. New York: Dekker pp.327-328.
     25 Hartigan. J.A. (1975): Clustering Algorithms New York : Wiley.
     26 Hawkins ,D.M. (1974) : The dection of errors in multivariate data using principal components. Journal of the American Statistical Association. Vol 59, pp.340-344.
     27 Hawkins, D.M. and Fatti, L.P. (1984) : Exploring multivariate data using the minor principal components, Statistican, Vol 33, pp.325-338.
     28 Hill, R.C. , Fomby, T.B. and Johnson, S.R. (1977): Component selection norms for principal components analysis. Communications in Statistics. Vol A6 , pp.309-334.
     29 Hotelling. H. (1933) Analysis of a complex of statistical variables into principal component. Journal of Educational Psychology, Vol 24 , pp.417-441 , 498-520.
     30 Hsuan. F.C. (1981) Ridge regression from principal component point of view. Communication in Statistics. Vol A10, pp.1981-1995.
     31 Jeffers. J.K.R. (1955): Principal component analysis in taxonomic research. Statistician, Vol 15, pp.207-208.
     32 Jeffers, J.N.R. (1967) : Two case studies in the application of principal component analysis. Applied Statistics, Vol 16, pp.225-236.
     33 Johnson, R.A. and Wichern, D.W. (1982): Applied Multivariate Statistical Analysis. New Jersey: Prentice -Hall, Inc.
     34 Jolliffe, I.T. (1972) :Discarding variabies in a principal component analysis I: Applied Statistics,Vol 21 ,pp.160-173
     35 Jolliffe, I.T. (1973) : Discarding variables in a principal component analysis 1I : Real data. Applied Statistics, Vol 22. pp.21-31.
     36 Jolliffe, LT. (986) Principal Component Analysis, New York: Springer-Verlag New York Inc.
     37 Lachenbruch. P.A. (975) : Discriminant Analysis, New York Hafner Press.
     38 Lowley, D.N. and Maxwell. A.E. (1971) Factor Analysis as a Statistical Method. 2nd ed. London: Butterworth.
     39 Halkovich. J.F. and Afifi, A.A. (1973): One tests for Multivariate normality. Journal of the American Statistical Association. Vol 68. PP. 176-179.
     40 Mansfield. E.R ., Webster. J.T. and Gunst. R.F. (1977) An analytic variable selection technique for principal component regression. Appl ied Statistics. Vol 26. PP. 34-40.
     41 Mardia. K. V. (1970) Measure of multivariate skewness and kurtosis with application. Biometrika. Vol 57. Pp. 519-530.
     42 Mardia. K.V .,Kent. J.T. and Bibby. J.M. (1979) : Multivariate Analysis. New York: Academic Press. PP. 394-406.
     43 Mezzich. J.E. and Solomon. H. (1980) Taxonomy and Behavioral Science: Comparative Performance of Grouping Methods. New York: Academic Press. Pp. 35-41.
     44 Morrison. D.F. (1967) Multivariate Statistical Methods. New York: McGraw-Hill. pp. 259-264.
     45 Mosteller. F. and Tukey. J.W. (1977) Regression: A Second Course in Statistics.
     Addison-Wesley. pp. 397-398. Data Analysis and Reading, MA: Addison-Wesley,pp.397-398 46 Muller. K.E. (1982) Understanding canonical correlation through the general linear model and principal components. American Statistican. Vol 36. PP. 342-354.
     47 Pearson. K. (1901): On lines and planes of closest fit to systems of points in place. Phil. Mag. (6) ,Vol 2. PP. 559-
     48 Press. S.J. (1972): Applied Multivariate Analysis. New York : Holt. Rinehart and Winston. PP. 305-319.
     49 Punj. G. and Stewart. D.W. (1983): Cluster analysis in marketing rese arch: review and suggestions for application. Journal of Marketing Research. Vol 10. PP. 144-145.
     50 Rao. C.R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya A. Vol 26. PP. 329-358.
     51 Rao, C.R. (1973) Linear Statistical Inference and Its Applications 2nd ed. New York: John Wiley, pp. 582-587.
     52 Tatsuoka, M.M. (1971) : Multivariate Analysis Techniques for Educational and Psychological Research, New York: John Wiley.
     53 Toro-Vizcarrondo, C. and Wallace, T.D. (1968): A test of the mean square error criterion for restrictions in linear regression, Journal of the American Statistical Association,
     Vol 63, PP. 558-572.
     54 Wallace, T.D. and Toro-Vizcarrondo, C. (1969) Tables for the mean squares error test for exact linear restrictions in regression, Jouranl of the American Statistical Association,
     Vol 64, PP. 1649-1663.
     55 Wolfe, J.H. (1970): Pattern clustering in multivariate mixture analysis, Multivariate Behaviour Research, Vol 5, PP. 329-350.
     56 Younger, M.S. (1979) : A First Course in Linear Regression. By Boston: Duxbury Press, PP. 479-481.
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/#B2002005725
資料類型 thesis
dc.contributor.advisor 葉小蓁zh_TW
dc.contributor.author (作者) 胡忠琳zh_TW
dc.creator (作者) 胡忠琳zh_TW
dc.date (日期) 1989en_US
dc.date.accessioned 4-五月-2016 14:23:56 (UTC+8)-
dc.date.available 4-五月-2016 14:23:56 (UTC+8)-
dc.date.issued (上傳時間) 4-五月-2016 14:23:56 (UTC+8)-
dc.identifier (其他 識別碼) B2002005725en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/90491-
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
     第二章 主成份分析……3
     第一節 主成份分析之原理……3
     第二節 主成份分析之文獻回顧……6
     第三節 有關主成份分析之性質與特性……7
     第三章 處理多元資料的方法……13
     第一節 多元資料的圖形表示法……13
     1.1 多元尺度分析法……14
     1.2 主坐標軸分析法……15
     1.3 雙向圖形表示法……19
     1.4 實例說明……22
     第二節 縮減與整理多元資料的方法……30
     2.1 使用主成份分析刪除成份或選擇變數的方法……30
     2.2 實例說明……32
     第三節 使用主成份分析處理多元資料中的離群值……37
     3.1 使用主成份尋找離群值的方法……37
     3.2 實例說明……42
     第四章 主成份分析在線型迴歸分析上的應用. ……48
     第一節 主成份迴歸模式的原理與性質……49
     第二節 主成份迴歸中成份的刪除原則……53
     第三節 比較不同偏迴歸估計式處理共線性的問題……61
     第四節 迴歸分析中使用主成份選擇重要變數的方法…… 66
     第五節 實例說明……72
     附錄……88
     第五章 主成份分析在區別分析與集群分析上的應用……92
     第一節 主成份在區別分析上的應用……93
     1.1 區別分析與主成份分析的相關性……93
     1.2 主成份應用於區別特殊型態母體資科的處理方法……96
     第二節 主成份在集群分析上的應用……103
     2.1 集群分析的簡介……103
     2.2 主成份應用於集群分析的方法……104
     2.3 實例說明…… 106
     附錄……119
     第六章 主成份分析在典型相關分析上的應用……128
     第一節 典型相關分析原理與主成份分析的相似性……128
     第二節 使用主成份處理典型相關分析的方法與貢獻……132
     實例說明……139
     第七章 結論……149
     附錄A……152
     附錄B……167
     附錄C……191
     附錄D……206
     參考文獻……210		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#B2002005725en_US
dc.title (題名) 主成份分析在各種多元統計方法上的應用zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 參考文獻
     一 、中文部分
     1 沈伊藤:主成份分析與其他統計分析之比較研究，台北:國立政治大學統計研究所碩士論文，民國七十五年。
     2 林清山:多變量分析統計法，台北:台灣東華書局，民國七十三年。
     3 黃俊英:多變量分析，三版，台北:中華經濟企業研究所 華泰經銷，民國七十七年。
     4 楊浩二. .多變量統計方法，台北:華泰書局，民國七三年。
     5 楊浩二:多變量常態檢定及應用之研究，台北:華泰書局民國七十一年。
     6 閩建蜀，游漢民：市場研究：基本方法，台北：巨浪出版社，民國七十五年。
     
     二、英文部分
     1 Anderberg. M.R. (1973) : Cluster Analysis for Applications. New York: Academic Press. pp. 185-190.
     2 Anderson. T.W. (1963) : Asymptotic theory for principal component analysis . Annals of Mathematical Statistics. Vol 34, PP. 122-148.
     3 Anderson. T.W. (1984) : An Introduction to Multivariate Statistical Analysis. New York: John Wiley and Son.
     4 Bartlett. M. S. (950) : Tests of significance in factor analysis. Brit. J. Psychol. Statist. Section. Vol 3. PP. 77
     5 Bartlett, M.S. (1951) : discriminant analysis. Vol 22 . pp.107-111. An inverse matrix adjustment in Annals of Mathematical Statistics.
     6 Berenson. H.L. Levine. D.H. and Goldstein (983): Intermediate Statistical Methods and Application A Computer Package Approach. Prentice - Hall. Inc .,Englewood Chiffs.
     New Jersey. pp.458-462.
     7 Cattell .R.B. (978): The Scientific Use of Factor Analysis in Beharioral and Life Sciences. New York: Plenum Press pP.15-39.
     8 Chang. W.C. (979) : Confidence interval estimation and transformation of data in a mixture of two multivariate normal distributions with any given large dimension. Technometrics,
     Vol 21 . PP. 351-355.
     9 Chang. W.C. (1983) : On using principal components before separating a mixture of two multivariate normal distributions. Applied Statistics. Vol 32, pp.267-275.
     10 Cravens. D.W., Woodruff. R.B. and Stamper. J.C. (1972) : An analytical approach for evaluating sales territory performance. Journal of Marketing. pp.31-37.
     11 Daling. J.R. and Tamura. H. (1970) Use of orthogonal factors for selection of variables in a regression equation an illustration. Applied Statistics. Vol 19. pp.260-268.
     12 Day. ICE. (969): Estimating the components of a mixture of normal distributions. Biometrika. Vol 56, pp.463-474.
     13 Draper. N.R.. and Smith. H. (981) :Analysis. 2nd ed. New York: John pp. 294-325. Applied Regression Wiley and Son Inc .,pp.294-325
     14 Dunham. R.B. (1977): Reaction to job characteristics: moderating effects of the origanization. Academy of Management Journal. Vol 20 .no. 1 . pp.42-65.
     15 Eastment. H.T.. and Krzanowski. W.J. (1982) Cross-Validatory choice of the number of components from a principal component analysis. Technometrics, Vol 24 ,pp.73-77
     16 Everitt. B.S. (1980) : Cluster Analysis. 2nd ed London Heinemann Educational Books.
     17 Filliben, J.J. (1975) The probability plot correlation coefficient test for normality. Technometrics. Vol 17. no. 1,pp.1l1-117.
     18 Finn. J.D. (1974): A General Model for Multivariate Analysis. New York Holt Rinehart and Winston. pp.182-204.
     19 Gabriel, K.R. (1971): The biplot graphic display of matrices by additive and multiplicative models. Journal of Royal Statistical Society B. Vol 40 . pp.186-196.
     20 Gnanadesikan. R. and Kettenring. J.R. (1972) Robust es – timates, residuals. and outlier detection with wultiresponse data. Biometrics. vol 28. pp.81-124.
     21 Goodnight. J. and Wallace. T.D. (1972) : Operational techniques and tables for making weak MSE tests for restrications in regression. Econometrica. Vol 40 . pp.699-709.
     22 Gordon. A.D. (1981) Classification Method for the Exploratory Analysis of Multivariate Data. London : Chapman and Hall.
     23 Gower. J.C. (1966) : Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika .Vol 53. pp.325-338 .
     24 Gunst. R. F. and Mason. R. L. (1980): Regress i on Analysis and Its Application: Data-Oriented Approach. New York: Dekker pp.327-328.
     25 Hartigan. J.A. (1975): Clustering Algorithms New York : Wiley.
     26 Hawkins ,D.M. (1974) : The dection of errors in multivariate data using principal components. Journal of the American Statistical Association. Vol 59, pp.340-344.
     27 Hawkins, D.M. and Fatti, L.P. (1984) : Exploring multivariate data using the minor principal components, Statistican, Vol 33, pp.325-338.
     28 Hill, R.C. , Fomby, T.B. and Johnson, S.R. (1977): Component selection norms for principal components analysis. Communications in Statistics. Vol A6 , pp.309-334.
     29 Hotelling. H. (1933) Analysis of a complex of statistical variables into principal component. Journal of Educational Psychology, Vol 24 , pp.417-441 , 498-520.
     30 Hsuan. F.C. (1981) Ridge regression from principal component point of view. Communication in Statistics. Vol A10, pp.1981-1995.
     31 Jeffers. J.K.R. (1955): Principal component analysis in taxonomic research. Statistician, Vol 15, pp.207-208.
     32 Jeffers, J.N.R. (1967) : Two case studies in the application of principal component analysis. Applied Statistics, Vol 16, pp.225-236.
     33 Johnson, R.A. and Wichern, D.W. (1982): Applied Multivariate Statistical Analysis. New Jersey: Prentice -Hall, Inc.
     34 Jolliffe, I.T. (1972) :Discarding variabies in a principal component analysis I: Applied Statistics,Vol 21 ,pp.160-173
     35 Jolliffe, I.T. (1973) : Discarding variables in a principal component analysis 1I : Real data. Applied Statistics, Vol 22. pp.21-31.
     36 Jolliffe, LT. (986) Principal Component Analysis, New York: Springer-Verlag New York Inc.
     37 Lachenbruch. P.A. (975) : Discriminant Analysis, New York Hafner Press.
     38 Lowley, D.N. and Maxwell. A.E. (1971) Factor Analysis as a Statistical Method. 2nd ed. London: Butterworth.
     39 Halkovich. J.F. and Afifi, A.A. (1973): One tests for Multivariate normality. Journal of the American Statistical Association. Vol 68. PP. 176-179.
     40 Mansfield. E.R ., Webster. J.T. and Gunst. R.F. (1977) An analytic variable selection technique for principal component regression. Appl ied Statistics. Vol 26. PP. 34-40.
     41 Mardia. K. V. (1970) Measure of multivariate skewness and kurtosis with application. Biometrika. Vol 57. Pp. 519-530.
     42 Mardia. K.V .,Kent. J.T. and Bibby. J.M. (1979) : Multivariate Analysis. New York: Academic Press. PP. 394-406.
     43 Mezzich. J.E. and Solomon. H. (1980) Taxonomy and Behavioral Science: Comparative Performance of Grouping Methods. New York: Academic Press. Pp. 35-41.
     44 Morrison. D.F. (1967) Multivariate Statistical Methods. New York: McGraw-Hill. pp. 259-264.
     45 Mosteller. F. and Tukey. J.W. (1977) Regression: A Second Course in Statistics.
     Addison-Wesley. pp. 397-398. Data Analysis and Reading, MA: Addison-Wesley,pp.397-398 46 Muller. K.E. (1982) Understanding canonical correlation through the general linear model and principal components. American Statistican. Vol 36. PP. 342-354.
     47 Pearson. K. (1901): On lines and planes of closest fit to systems of points in place. Phil. Mag. (6) ,Vol 2. PP. 559-
     48 Press. S.J. (1972): Applied Multivariate Analysis. New York : Holt. Rinehart and Winston. PP. 305-319.
     49 Punj. G. and Stewart. D.W. (1983): Cluster analysis in marketing rese arch: review and suggestions for application. Journal of Marketing Research. Vol 10. PP. 144-145.
     50 Rao. C.R. (1964) The use and interpretation of principal component analysis in applied research. Sankhya A. Vol 26. PP. 329-358.
     51 Rao, C.R. (1973) Linear Statistical Inference and Its Applications 2nd ed. New York: John Wiley, pp. 582-587.
     52 Tatsuoka, M.M. (1971) : Multivariate Analysis Techniques for Educational and Psychological Research, New York: John Wiley.
     53 Toro-Vizcarrondo, C. and Wallace, T.D. (1968): A test of the mean square error criterion for restrictions in linear regression, Journal of the American Statistical Association,
     Vol 63, PP. 558-572.
     54 Wallace, T.D. and Toro-Vizcarrondo, C. (1969) Tables for the mean squares error test for exact linear restrictions in regression, Jouranl of the American Statistical Association,
     Vol 64, PP. 1649-1663.
     55 Wolfe, J.H. (1970): Pattern clustering in multivariate mixture analysis, Multivariate Behaviour Research, Vol 5, PP. 329-350.
     56 Younger, M.S. (1979) : A First Course in Linear Regression. By Boston: Duxbury Press, PP. 479-481.		zh_TW