Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)
Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s) Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)
References 423Castro, P.E., Lawton, W.H., and Sylvestre, E.A. (1986). Principal modesof variation for processes with continuous sample curves. Technometrics,28, 329–337.Cattell, R.B. (1966). The scree test for the number of factors. Multiv.Behav. Res., 1, 245–276.Cattell, R.B. (1978). The Scientific Use of Factor Analysis in Behavioraland Life Sciences. New York: Plenum Press.Cattell, R.B. and Vogelmann, S. (1977). A comprehensive trial of the screeand KG criteria for determining the number of factors. Mult. Behav.Res., 12, 289–325.Caussinus, H. (1986). Models and uses of principal component analysis: Acomparison emphasizing graphical displays and metric choices. In MultidimensionalData Analysis, eds. J. de Leeuw, W. Heiser, J. Meulmanand F. Critchley, 149–178. Leiden: DSWO Press.Caussinus, H. (1987). Discussion of ‘What is projection pursuit?’ by Jonesand Sibson. J. R. Statist. Soc. A, 150, 26.Caussinus, H. and Ferré, L. (1992). Comparing the parameters of a modelfor several units by means of principal component analysis. Computat.Statist. Data Anal., 13, 269–280.Caussinus, H., Hakam, S. and Ruiz-Gazen, A. (2001). Projections révélatricescontrôlées. Recherche d’individus atypiques. To appear in Rev.Statistique Appliquée.Caussinus, H. and Ruiz, A. (1990) Interesting projections of multidimensionaldata by means of generalized principal component analysis.In COMPSTAT 90, eds. K. Momirovic and V. Mildner, 121–126.Heidelberg: Physica-Verlag.Caussinus, H. and Ruiz-Gazen, A. (1993). Projection pursuit and generalizedprincipal component analysis. In New Directions in Statistical DataAnalysis and Robustness, eds. S. Morgenthaler, E. Ronchetti and W.A.Stahel, 35–46. Basel: Birkhäuser Verlag.Caussinus, H. and Ruiz-Gazen, A. (1995). Metrics for finding typical structuresby means of principal component analysis. In Data Science and ItsApplication, eds. Y. Escoufier, B. Fichet, E. Diday, L. Lebart, C. Hayashi,N. Ohsumi and Y. Baba, 177–192. Tokyo: Academic Press.Chambers, J.M. (1977). Computational Methods for Data Analysis. NewYork: Wiley.Chambers, J.M., Cleveland, W.S., Kleiner, B. and Tukey, P.A. (1983).Graphical Methods for Data Analysis. Belmont: Wadsworth.Champely, S. and Doledec, S. (1997). How to separate long-term trendsfrom periodic variation in water quality monitoring. Water Res., 11,2849–2857.Chang, W.-C. (1983). On using principal components before separatinga mixture of two multivariate normal distributions. Appl. Statist., 32,267–275.
424 ReferencesChatfield, C. and Collins, A.J. (1989). Introduction to MultivariateAnalysis. London: Chapman and Hall.Cheng, C.-L. and van Ness, J.W. (1999). Statistical Regression withMeasurement Error. London: Arnold.Chernoff, H. (1973). The use of faces to represent points in k-dimensionalspace graphically. J. Amer. Statist. Assoc., 68, 361–368.Cherry, S. (1997). Some comments on singular value decompositionanalysis. J. Climate, 10, 1759–1761.Chipman, H.A. and Gu, H. (2002). Interpretable dimension reduction. Toappear in J. Appl. Statist.Chouakria, A., Cazes, P. and Diday, E. (2000). Symbolic principal componentanalysis. In Analysis of Symbolic Data. Exploratory Methods forExtracting Statistical Information from Complex Data, eds. H.-H. Bockand E. Diday, 200–212. Berlin: Springer-Verlag.Clausen, S.-E. (1998). Applied Correspondence Analysis: An Introduction.Thousand Oaks: Sage.Cleveland, W.S. (1979). Robust locally weighted regression and smoothingscatterplots. J. Amer. Statist. Assoc., 74, 829–836.Cleveland, W.S. (1981). LOWESS: A program for smoothing scatterplotsby robust locally weighted regression. Amer. Statistician, 35, 54.Cleveland, W.S. and Guarino, R. (1976). Some robust statistical proceduresand their application to air pollution data. Technometrics, 18, 401–409.Cochran, R.N. and Horne, F.H., (1977). Statistically weighted principalcomponent analysis of rapid scanning wavelength kinetics experiments.Anal. Chem., 49, 846–853.Cohen, S.J. (1983). Classification of 500 mb height anomalies usingobliquely rotated principal components. J. Climate Appl. Meteorol., 22,1975–1988.Cohn, R.D. (1999). Comparisons of multivariate relational structures inserially correlated data. J. Agri. Biol. Environ. Statist., 4, 238–257.Coleman, D. (1985). Hotelling’s T 2 , robust principal components, andgraphics for SPC. Paper presented at the 1985 Annual Meeting of theAmerican Statistical Association.Commandeur, J.J.F, Groenen, P.J.F and Meulman, J.J. (1999). A distancebasedvariety of nonlinear multivariate data analysis, including weightsfor objects and variables. Psychometrika, 64, 169–186.Compagnucci, R.H., Araneo, D. and Canziani, P.O. (2001). Principal sequencepattern analysis: A new approach to classifying the evolution ofatmospheric systems. Int. J. Climatol., 21, 197–217.Compagnucci, R.H. and Salles, M.A. (1997). Surface pressure patternsduring the year over Southern South America. Int. J. Climatol., 17,635–653.Cook, R.D. and Weisberg, S. (1982). Residuals and Influence in Regression.New York: Chapman and Hall.
- Page 404 and 405: 14Generalizations and Adaptations o
- Page 406 and 407: 14.1. Non-Linear Extensions of Prin
- Page 408 and 409: 14.1. Additive Principal Components
- Page 410 and 411: 14.1. Additive Principal Components
- Page 412 and 413: 14.1. Additive Principal Components
- Page 414 and 415: 14.2. Weights, Metrics, Transformat
- Page 416 and 417: 14.2. Weights, Metrics, Transformat
- Page 418 and 419: 14.2. Weights, Metrics, Transformat
- Page 420 and 421: 14.2. Weights, Metrics, Transformat
- Page 422 and 423: 14.2. Weights, Metrics, Transformat
- Page 424 and 425: 14.3. PCs in the Presence of Second
- Page 426 and 427: 14.4. PCA for Non-Normal Distributi
- Page 428 and 429: 14.5. Three-Mode, Multiway and Mult
- Page 430 and 431: 14.5. Three-Mode, Multiway and Mult
- Page 432 and 433: 14.6. Miscellanea 401• Linear App
- Page 434 and 435: 14.6. Miscellanea 40314.6.3 Regress
- Page 436 and 437: 14.7. Concluding Remarks 405space o
- Page 438 and 439: Appendix AComputation of Principal
- Page 440 and 441: A.1. Numerical Calculation of Princ
- Page 442 and 443: A.1. Numerical Calculation of Princ
- Page 444 and 445: A.1. Numerical Calculation of Princ
- Page 446 and 447: ReferencesAguilera, A.M., Gutiérre
- Page 448 and 449: References 417Apley, D.W. and Shi,
- Page 450 and 451: References 419Benasseni, J. (1986b)
- Page 452 and 453: References 421Boik, R.J. (1986). Te
- Page 456 and 457: References 425Cook, R.D. (1986). As
- Page 458 and 459: References 427Dempster, A.P., Laird
- Page 460 and 461: References 429Feeney, G.J. and Hest
- Page 462 and 463: References 431in Descriptive Multiv
- Page 464 and 465: References 433Gunst, R.F. and Mason
- Page 466 and 467: References 435Hocking, R.R., Speed,
- Page 468 and 469: References 437Jeffers, J.N.R. (1978
- Page 470 and 471: References 439Kazi-Aoual, F., Sabat
- Page 472 and 473: References 441Krzanowski, W.J. (200
- Page 474 and 475: References 443Mann, M.E. and Park,
- Page 476 and 477: References 445Monahan, A.H., Tangan
- Page 478 and 479: References 447Pack, P., Jolliffe, I
- Page 480 and 481: References 449Richman M.B. (1993).
- Page 482 and 483: References 451Soofi, E.S. (1988). P
- Page 484 and 485: References 453Tenenbaum, J.B., de S
- Page 486 and 487: References 455Vong, R., Geladi, P.,
- Page 488 and 489: References 457regularities in multi
- Page 490 and 491: Index 459116, 127-130, 133, 270, 27
- Page 492 and 493: Index 461computationin (PC) regress
- Page 494 and 495: Index 463discriminant principal com
- Page 496 and 497: Index 465of correlations between va
- Page 498 and 499: Index 467see also hypothesis testin
- Page 500 and 501: Index 469PC algorithms with noise 4
- Page 502 and 503: Index 471in functional and structur
References 423Castro, P.E., Lawton, W.H., and Sylvestre, E.A. (1986). <strong>Principal</strong> modesof variation for processes with continuous sample curves. Technometrics,28, 329–337.Cattell, R.B. (1966). The scree test for the number of factors. Multiv.Behav. Res., 1, 245–276.Cattell, R.B. (1978). The Scientific Use of Factor <strong>Analysis</strong> in Behavioraland Life Sciences. New York: Plenum Press.Cattell, R.B. and Vogelmann, S. (1977). A comprehensive trial of the screeand KG criteria for determining the number of factors. Mult. Behav.Res., 12, 289–325.Caussinus, H. (1986). Models and uses of principal component analysis: Acomparison emphasizing graphical displays and metric choices. In MultidimensionalData <strong>Analysis</strong>, eds. J. de Leeuw, W. Heiser, J. Meulmanand F. Critchley, 149–178. Leiden: DSWO Press.Caussinus, H. (1987). Discussion of ‘What is projection pursuit?’ by Jonesand Sibson. J. R. Statist. Soc. A, 150, 26.Caussinus, H. and Ferré, L. (1992). Comparing the parameters of a modelfor several units by means of principal component analysis. Computat.Statist. Data Anal., 13, 269–280.Caussinus, H., Hakam, S. and Ruiz-Gazen, A. (2001). Projections révélatricescontrôlées. Recherche d’individus atypiques. To appear in Rev.Statistique Appliquée.Caussinus, H. and Ruiz, A. (1990) Interesting projections of multidimensionaldata by means of generalized principal component analysis.In COMPSTAT 90, eds. K. Momirovic and V. Mildner, 121–126.Heidelberg: Physica-Verlag.Caussinus, H. and Ruiz-Gazen, A. (1993). Projection pursuit and generalizedprincipal component analysis. In New Directions in Statistical Data<strong>Analysis</strong> and Robustness, eds. S. Morgenthaler, E. Ronchetti and W.A.Stahel, 35–46. Basel: Birkhäuser Verlag.Caussinus, H. and Ruiz-Gazen, A. (1995). Metrics for finding typical structuresby means of principal component analysis. In Data Science and ItsApplication, eds. Y. Escoufier, B. Fichet, E. Diday, L. Lebart, C. Hayashi,N. Ohsumi and Y. Baba, 177–192. Tokyo: Academic Press.Chambers, J.M. (1977). Computational Methods for Data <strong>Analysis</strong>. NewYork: Wiley.Chambers, J.M., Cleveland, W.S., Kleiner, B. and Tukey, P.A. (1983).Graphical Methods for Data <strong>Analysis</strong>. Belmont: Wadsworth.Champely, S. and Doledec, S. (1997). How to separate long-term trendsfrom periodic variation in water quality monitoring. Water Res., 11,2849–2857.Chang, W.-C. (1983). On using principal components before separatinga mixture of two multivariate normal distributions. Appl. Statist., 32,267–275.
Change language