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References
                              456
                              Webster, J.T., Gunst, R.F. and Mason, R.L. (1974). Latent root regression
                                analysis. Technometrics, 16, 513–522.
                              White, D., Richman, M. and Yarnal, B. (1991). Climate regionalization
                                and rotation of principal components. Int. J. Climatol., 11, 1–25.
                              White, J.W. and Gunst, R.F. (1979). Latent root regression: Large sample
                                analysis. Technometrics, 21, 481–488.
                              Whittaker, J. (1990). Graphical Models in Applied Multivariate Analysis.
                                Chichester: Wiley.
                              Whittle, P. (1952). On principal components and least squares methods of
                                factor analysis. Skand. Actuar., 35, 223–239.
                              Wiberg, T. (1976). Computation of principal components when data are
                                missing. In Compstat 1976, eds. J. Gordesch and P. Naeve, 229–236.
                                Wien: Physica-Verlag.
                              Widaman, K.F. (1993). Common factor analysis versus principal compo-
                                nent analysis: Differential bias in representing model parameters. Mult.
                                Behav. Res., 28, 263–311.
                              Wigley, T.M.L., Lough, J.M. and Jones, P.D. (1984). Spatial patterns of
                                precipitation in England and Wales and a revised, homogeneous England
                                and Wales precipitation series. J. Climatol., 4, 1–25.
                              Wikle, C.K. and Cressie, N. (1999). A dimension-reduced approach to
                                space-time Kalman filtering. Biometrika, 86, 815–829.
                              Wilkinson, J.H. (1965). The Algebraic Eigenvalue Problem. Oxford: Oxford
                                University Press.
                              Wilkinson, J.H. and Reinsch, C. (1971). Handbook for Automatic Compu-
                                tation, Vol. 11, Linear Algebra. Berlin: Springer-Verlag.
                              Winsberg, S. (1988). Two techniques: Monotone spline transformations
                                for dimension reduction in PCA and easy-to generate metrics for
                                PCA of sampled functions. In Component and Correspondence Analy-
                                sis. Dimension Reduction by Functional Approximation, eds. J.L.A. van
                                Rijckevorsel and J. de Leeuw, 115–135. Chichester: Wiley.
                              Witten, I.H. and Frank, E. (2000). Data Mining. Practical Machine Learn-
                                ing Tools and Techniques with Java Implementations. San Francisco:
                                Morgan Kaufmann.
                              Wold, H. (1984). Partial least squares. In Encyclopedia of Statistical Sci-
                                ence, Vol 6, eds. N. L. Johnson and S. Kotz, 581–591. New York:
                                Wiley.
                              Wold, S. (1976). Pattern recognition by means of disjoint principal
                                components models. Patt. Recog., 8, 127–139.
                              Wold, S. (1978). Cross-validatory estimation of the number of components
                                in factor and principal components models. Technometrics, 20, 397–405.
                              Wold, S. (1994). Exponentially weighted moving principal components
                                analysis and projections to latent structures. Chemometrics Intell. Lab.
                                Syst., 23, 149–161.
                              Wold, S., Albano, C., Dunn, W.J., Esbensen, K., Hellberg, S., Johans-
                                son, E. and Sj¨ostr¨om, M. (1983). Pattern recognition: Finding and using
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