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

442 ReferencesLefkovitch, L.P. (1993). Concensus principal components. Biom. J., 35,567–580.Legates, D.R. (1991). The effect of domain shape on principal componentanalyses. Int. J. Climatol., 11, 135-146.Legates, D.R. (1993). The effect of domain shape on principal componentanalyses: A reply. Int. J. Climatol., 13, 219–228.Legendre, L. and Legendre, P. (1983). Numerical Ecology. Amsterdam:Elsevier.Lewis-Beck, M.S. (1994). Factor Analysis and Related Techniques. London:Sage.Li, G. and Chen, Z. (1985). Projection-pursuit approach to robust dispersionmatrices and principal components: Primary theory and MonteCarlo. J. Amer. Statist. Assoc., 80, 759–766 (correction 80, 1084).Li, K.-C., Lue, H.-H. and Chen, C.-H. (2000). Interactive tree-structuredregression via principal Hessian directions. J. Amer. Statist. Assoc., 95,547–560.Little, R.J.A. (1988). Robust estimation of the mean and covariance matrixfrom data with missing values. Appl. Statist., 37, 23–38.Little, R.J.A. and Rubin, D.B. (1987). Statistical Analysis with MissingData. New York: Wiley.Locantore, N., Marron, J.S., Simpson, D.G., Tripoli, N., Zhang, J.T. andCohen, K.L. (1999). Robust principal component analysis for functionaldata. Test, 8, 1–73 (including discussion).Lott, W.F. (1973). The optimal set of principal component restrictions ona least squares regression. Commun. Statist., 2, 449–464.Lu, J., Ko, D. and Chang, T. (1997). The standardized influence matrixand its applications. J. Amer. Statist. Assoc., 92, 1572–1580.Lynn, H.S. and McCulloch, C.E. (2000). Using principal component analysisand correspondence analysis for estimation in latent variable models.J. Amer. Statist. Assoc., 95, 561–572.Macdonell, W.R. (1902). On criminal anthropometry and the identificationof criminals. Biometrika, 1, 177–227.Mager, P.P. (1980a). Principal component regression analysis applied instructure-activity relationships 2. Flexible opioids with unusually highsafety margin. Biom. J., 22, 535–543.Mager, P.P. (1980b). Correlation between qualitatively distributed predictingvariables and chemical terms in acridine derivatives using principalcomponent analysis. Biom. J., 22, 813–825.Mandel, J. (1971). A new analysis of variance model for non-additive data.Technometrics, 13, 1–18.Mandel, J. (1972). Principal components, analysis of variance and datastructure. Statistica Neerlandica, 26, 119–129.Mandel, J. (1982). Use of the singular value decomposition in regressionanalysis. Amer. Statistician, 36, 15–24.