Page 477 - Jolliffe I. Principal Component Analysis

References
442
Lefkovitch, L.P. (1993). Concensus principal components. Biom. J., 35,
567–580.
Legates, D.R. (1991). The effect of domain shape on principal component
analyses. Int. J. Climatol., 11, 135-146.
Legates, D.R. (1993). The effect of domain shape on principal component
analyses: 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 dis-
persion matrices and principal components: Primary theory and Monte
Carlo. J. Amer. Statist. Assoc., 80, 759–766 (correction 80, 1084).
Li, K.-C., Lue, H.-H. and Chen, C.-H. (2000). Interactive tree-structured
regression via principal Hessian directions. J. Amer. Statist. Assoc., 95,
547–560.
Little, R.J.A. (1988). Robust estimation of the mean and covariance matrix
from data with missing values. Appl. Statist., 37, 23–38.
Little, R.J.A. and Rubin, D.B. (1987). Statistical Analysis with Missing
Data. New York: Wiley.
Locantore, N., Marron, J.S., Simpson, D.G., Tripoli, N., Zhang, J.T. and
Cohen, K.L. (1999). Robust principal component analysis for functional
data. Test, 8, 1–73 (including discussion).
Lott, W.F. (1973). The optimal set of principal component restrictions on
a least squares regression. Commun. Statist., 2, 449–464.
Lu, J., Ko, D. and Chang, T. (1997). The standardized influence matrix
and its applications. J. Amer. Statist. Assoc., 92, 1572–1580.
Lynn, H.S. and McCulloch, C.E. (2000). Using principal component analy-
sis and correspondence analysis for estimation in latent variable models.
J. Amer. Statist. Assoc., 95, 561–572.
Macdonell, W.R. (1902). On criminal anthropometry and the identification
of criminals. Biometrika, 1, 177–227.
Mager, P.P. (1980a). Principal component regression analysis applied in
structure-activity relationships 2. Flexible opioids with unusually high
safety margin. Biom. J., 22, 535–543.
Mager, P.P. (1980b). Correlation between qualitatively distributed predict-
ing variables and chemical terms in acridine derivatives using principal
component 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 data
structure. Statistica Neerlandica, 26, 119–129.
Mandel, J. (1982). Use of the singular value decomposition in regression
analysis. Amer. Statistician, 36, 15–24.