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Castro, P.E., Lawton, W.H., and Sylvestre, E.A. (1986). Principal modes
                                of variation for processes with continuous sample curves. Technometrics,
                                28, 329–337.                                   References   423
                              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 Behavioral
                                and Life Sciences. New York: Plenum Press.
                              Cattell, R.B. and Vogelmann, S. (1977). A comprehensive trial of the scree
                                and KG criteria for determining the number of factors. Mult. Behav.
                                Res., 12, 289–325.
                              Caussinus, H. (1986). Models and uses of principal component analysis: A
                                comparison emphasizing graphical displays and metric choices. In Mul-
                                tidimensional Data Analysis, eds. J. de Leeuw, W. Heiser, J. Meulman
                                and F. Critchley, 149–178. Leiden: DSWO Press.
                              Caussinus, H. (1987). Discussion of ‘What is projection pursuit?’ by Jones
                                and Sibson. J. R. Statist. Soc. A, 150, 26.
                              Caussinus, H. and Ferr´e, L. (1992). Comparing the parameters of a model
                                for 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 r´ev´e-
                                latrices contrˆol´ees. Recherche d’individus atypiques. To appear in Rev.
                                Statistique Appliqu´ee.
                              Caussinus, H. and Ruiz, A. (1990) Interesting projections of multidi-
                                mensional data 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 general-
                                ized principal component analysis. In New Directions in Statistical Data
                                Analysis and Robustness, eds. S. Morgenthaler, E. Ronchetti and W.A.
                                Stahel, 35–46. Basel: Birkh¨auser Verlag.
                              Caussinus, H. and Ruiz-Gazen, A. (1995). Metrics for finding typical struc-
                                tures by means of principal component analysis. In Data Science and Its
                                Application, 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.New
                                York: 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 trends
                                from periodic variation in water quality monitoring. Water Res., 11,
                                2849–2857.
                              Chang, W.-C. (1983). On using principal components before separating
                                a mixture of two multivariate normal distributions. Appl. Statist., 32,
                                267–275.
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