Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)

430 ReferencesFomby, T.B., Hill, R.C. and Johnson, S.R. (1978). An optimal property ofprincipal components in the context of restricted least squares. J. Amer.Statist. Assoc., 73, 191–193.Foster, P. (1998). Exploring multivariate data using directions of highdensity. Statist. Computing, 8, 347–355.Fowlkes, E.B. and Kettenring, J.R. (1985). Comment on ‘Estimating optimaltransformations for multiple regression and correlation’ by L.Breiman and J.H. Friedman. J. Amer. Statist. Assoc., 80, 607–613.Frane, J.W. (1976). Some simple procedures for handling missing data inmultivariate analysis. Psychometrika, 41, 409–415.Frank, I.E. and Friedman, J.H. (1989). Classification: Oldtimers andnewcomers. J. Chemometrics, 3, 463–475.Frank, I.E. and Friedman, J.H. (1993). A statistical view of somechemometrics tools. Technometrics, 35, 109–148 (including discussion).Franklin, S.B., Gibson, D.J., Robertson, P.A., Pohlmann, J.T. and Fralish,J.S. (1995). Parallel analysis: A method for determining significantprincipal components. J. Vegetat. Sci., 6, 99–106.Freeman, G.H. (1975). Analysis of interactions in incomplete two-waytables. Appl. Statist., 24, 46–55.Friedman, D.J. and Montgomery, D.C. (1985). Evaluation of the predictiveperformance of biased regression estimators. J. Forecasting, 4, 153-163.Friedman, J.H. (1987). Exploratory projection pursuit. J. Amer. Statist.Assoc., 82, 249–266.Friedman, J.H. (1989). Regularized discriminant analysis. J. Amer. Statist.Assoc., 84, 165–175.Friedman, J.H. and Tukey, J.W. (1974). A projection pursuit algorithm forexploratory data analysis. IEEE Trans. Computers C, 23, 881–889.Friedman, S. and Weisberg, H.F. (1981). Interpreting the first eigenvalueof a correlation matrix. Educ. Psychol. Meas., 41, 11–21.Frisch, R. (1929). Correlation and scatter in statistical variables. NordicStatist. J., 8, 36–102.Fujikoshi, Y., Krishnaiah, P.R. and Schmidhammer, J. (1985). Effect ofadditional variables in principal component analysis, discriminant analysisand canonical correlation analysis. Tech. Report 85-31, Center forMultivariate Analysis, University of Pittsburgh.Gabriel, K.R. (1971). The biplot graphic display of matrices withapplication to principal component analysis. Biometrika, 58, 453–467.Gabriel, K.R. (1978). Least squares approximation of matrices by additiveand multiplicative models. J. R. Statist. Soc. B, 40, 186–196.Gabriel, K.R. (1981). Biplot display of multivariate matrices for inspectionof data and diagnosis. In Interpreting Multivariate Data, ed. V. Barnett,147–173. Chichester: Wiley.Gabriel K.R. (1995a). Biplot display of multivariate categorical data,with comments on multiple correspondence analysis. In Recent Advances