Page 480 - Jolliffe I. Principal Component Analysis
P. 480

Monahan, A.H., Tangang, F.T. and Hsieh, W.W. (1999). A potential prob-
                                lem with extended EOF analysis of standing wave fields. Atmos.–Ocean,
                                37, 241–254.                                   References   445
                              Mori, Y., Iizuka, M., Tarumi, T. and Tanaka, Y. (1999). Variable selection
                                in “principal component analysis based on a subset of variables”. Bul-
                                letin of the International Statistical Institute 52nd Session Contributed
                                Papers, Tome LVIII, Book 2, 333–334.
                              Mori, Y., Iizuka, M., Tarumi, T. and Tanaka, Y. (2000). Study of variable
                                selection criteria in data analysis. Proc. 10th Japan and Korea Joint
                                Conference of Statistics, 547–554.
                              Mori, Y., Tanaka, Y. and Tarumi, T. (1998). Principal component analysis
                                based on a subset of variables for qualitative data. In Data Science,
                                Classification, and Related Methods, eds. C. Hayashi, N. Ohsumi, K.
                                Yajima, Y. Tanaka, H.H. Bock and Y. Baba, 547–554. Tokyo: Springer-
                                Verlag.
                              Morgan, B.J.T. (1981). Aspects of QSAR: 1. Unpublished report, CSIRO
                                Division of Mathematics and Statistics, Melbourne.
                              Morrison, D.F. (1976). Multivariate Statistical Methods, 2nd edition. Tokyo:
                                McGraw-Hill Kogakusha.
                              Moser, C.A. and Scott, W. (1961). British Towns. Edinburgh: Oliver and
                                Boyd.
                              Mosteller, F. and Tukey, J.W. (1977). Data Analysis and Regression: A
                                Second Course in Statistics. Reading, MA: Addison-Wesley.
                              Mote, P.W., Clark, H.L., Dunkerton, T.J., Harwood, R.S., and Pumphrey,
                                H.C. (2000). Intraseasonal variations of water vapor in the tropical upper
                                troposphere and tropopause region. J. Geophys. Res., 105, 17457–17470.
                              Muller, K.E. (1981). Relationships between redundancy analysis, canonical
                                correlation and multivariate regression. Psychometrika, 46, 139–142.
                              Muller, K.E. (1982). Understanding canonical correlation through the gen-
                                eral linear model and principal components. Amer. Statistician, 36,
                                342–354.
                              Naes, T. (1985). Multivariate calibration when the error covariance matrix
                                is structured. Technometrics, 27, 301–311.
                              Naes, T. and Helland, I.S. (1993). Relevant components in regression.
                                Scand. J. Statist., 20, 239–250.
                              Naes, T., Irgens, C. and Martens, H. (1986). Comparison of linear statistical
                                methods for calibration of NIR instruments. Appl. Statist., 35, 195–206.
                              Naes, T. and Isaksson, T. (1991). Splitting of calibration data by cluster
                                analysis. J. Chemometrics, 5, 49–65.
                              Naes, T. and Isaksson, T. (1992). Locally weighted regression in diffuse
                                near-infrared transmittance spectroscopy. Appl. Spectroscopy, 46, 34–43.
                              Naga, R.A. and Antille, G. (1990). Stability of robust and non-robust
                                principal components analysis. Computat. Statist. Data Anal., 10,
                                169–174.
   475   476   477   478   479   480   481   482   483   484   485