Page 468 - Jolliffe I. Principal Component Analysis
P. 468

433
                                                                               References
                              Gunst, R.F. and Mason, R.L. (1977b). Advantages of examining multi-
                                collinearities in regression analysis. Biometrics, 33, 249–260.
                              Gunst, R.F. and Mason, R.L. (1979). Some considerations in the evaluation
                                of alternative prediction equations. Technometrics, 21, 55–63.
                              Gunst, R.F. and Mason, R.L. (1980). Regression Analysis and Its
                                Applications: A Data-Oriented Approach. New York: Dekker.
                              Gunst, R.F., Webster, J.T. and Mason, R.L. (1976). A comparison of least
                                squares and latent root regression estimators. Technometrics, 18, 75–
                                83.
                              Guttorp, P. and Sampson, P.D. (1994). Methods for estimating heteroge-
                                neous spatial covariance functions with environmental applications. In
                                Handbook of Statistics, Vol. 12, eds. G.P. Patil and C.R. Rao, 661–689.
                                Amsterdam: Elsevier.
                              Hadi, A.S. and Nyquist, H. (1993). Further theoretical results and a com-
                                parison between two methods for approximating eigenvalues of perturbed
                                covariance matrices. Statist. Computing, 3, 113–123.
                              Hadi, A.S. and Ling, R.F. (1998). Some cautionary notes on the use of
                                principal components regression. Amer. Statistician, 52, 15–19.
                              Hall, P., Poskitt, D.S., and Presnell, B. (2001). A functional data-analytic
                                approach to signal discrimination. Technometrics, 43, 1–9.
                              Hamilton, J.D. (1994). Time Series Analysis. Princeton: Princeton Univer-
                                sity Press.
                              Hampel, F.R. (1974). The influence curve and its role in robust estimation.
                                J. Amer. Statist. Assoc., 69, 383–393.
                              Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. and Stahel, W.A. (1986).
                                Robust Statistics: The Approach Based on Influence Functions.New
                                York: Wiley.
                              Hand, D.J. (1982). Kernel Discriminant Analysis. Chichester: Research
                                Studies Press.
                              Hand, D.J. (1998). Data mining: Statistics and more? Amer. Statistician,
                                52, 112–118.
                              Hand, D, Mannila, H. and Smyth, P. (2001). Principles of Data Mining.
                                Cambridge: MIT Press.
                              Hannachi, A. (2000). Probabilistic-based approach to optimal filtering.
                                Phys. Rev. E, 61, 3610–3619.
                              Hannachi, A. and O’Neill, A. (2001). Atmospheric multiple equilibria and
                                non-Gaussian behaviour in model simulations. Q.J.R. Meteorol. Soc. 127,
                                939-958.
                              Hansch, C., Leo, A., Unger, S.H., Kim, K.H., Nikaitani, D. and Lien,
                                E.J. (1973). ‘Aromatic’ substituent constants for structure–activity
                                correlations. J. Medicinal Chem., 16, 1207–1216.
                              Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric re-
                                sponse studies. In Meteorology Over the Tropical Oceans, ed. B. D. Shaw,
                                251–259. Bracknell: Royal Meteorological Society.
   463   464   465   466   467   468   469   470   471   472   473