Page 465 - Jolliffe I. Principal Component Analysis
P. 465
References
430
Fomby, T.B., Hill, R.C. and Johnson, S.R. (1978). An optimal property of
principal components in the context of restricted least squares. J. Amer.
Statist. Assoc., 73, 191–193.
Foster, P. (1998). Exploring multivariate data using directions of high
density. Statist. Computing, 8, 347–355.
Fowlkes, E.B. and Kettenring, J.R. (1985). Comment on ‘Estimating op-
timal transformations 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 in
multivariate analysis. Psychometrika, 41, 409–415.
Frank, I.E. and Friedman, J.H. (1989). Classification: Oldtimers and
newcomers. J. Chemometrics, 3, 463–475.
Frank, I.E. and Friedman, J.H. (1993). A statistical view of some
chemometrics tools. Technometrics, 35, 109–148 (including discussion).
Franklin, S.B., Gibson, D.J., Robertson, P.A., Pohlmann, J.T. and Fral-
ish, J.S. (1995). Parallel analysis: A method for determining significant
principal components. J. Vegetat. Sci., 6, 99–106.
Freeman, G.H. (1975). Analysis of interactions in incomplete two-way
tables. Appl. Statist., 24, 46–55.
Friedman, D.J. and Montgomery, D.C. (1985). Evaluation of the predictive
performance 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 for
exploratory data analysis. IEEE Trans. Computers C, 23, 881–889.
Friedman, S. and Weisberg, H.F. (1981). Interpreting the first eigenvalue
of a correlation matrix. Educ. Psychol. Meas., 41, 11–21.
Frisch, R. (1929). Correlation and scatter in statistical variables. Nordic
Statist. J., 8, 36–102.
Fujikoshi, Y., Krishnaiah, P.R. and Schmidhammer, J. (1985). Effect of
additional variables in principal component analysis, discriminant anal-
ysis and canonical correlation analysis. Tech. Report 85-31, Center for
Multivariate Analysis, University of Pittsburgh.
Gabriel, K.R. (1971). The biplot graphic display of matrices with
application to principal component analysis. Biometrika, 58, 453–467.
Gabriel, K.R. (1978). Least squares approximation of matrices by additive
and multiplicative models. J. R. Statist. Soc. B, 40, 186–196.
Gabriel, K.R. (1981). Biplot display of multivariate matrices for inspection
of 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

