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

Index 461computationin (PC) regression 46, 168, 170,173, 182, 412, 413of factors 154, 364of PCs 7, 8, 29, 46, 90, 364efficient calculation 407–414in computer packages 407, 408parallel processing 413, 414using neural networks 400, 413,414see also biplots, correspondenceanalysis, principal coordinateanalysis, singularvalue decompositioncomputationally intensive methods112, 120-127see also bootstrap,cross-validation, jackknifecomputer languages 407-412computer packages/libraries/software25,66, 150, 153, 162, 271, 407,408BMDP 407EISPACK 411IMSL 411MATLAB 407NAG 411PRINCALS 375R/S-PLUS 407-412SAS 407SPSS 375, 407computer science 79concentration ellipses 97, 102concurvities 378conditional covariance orcorrelation matrix 15, 139confidence intervals, see intervalestimationcongruence coefficient 357consensus components 356constraints on PCs 393, 401constrained PCA 401contrast, homogeneous andsparsity constraints 295,296soft constraints 401contingency tables 79, 103, 106,107, 340–343, 375, 385see also interactions, principalaxescontinuum regression 184contours of constant probability16, 18, 23, 33, 39, 54, 367,368enclosing maximum volume 16contrasts between variables 42, 57,58, 67, 76, 81, 99, 162, 217,244, 245, 294, 297, 344, 347,349convex hulls 81, 82, 214correlation between PCs andvariables 25, 141, 214, 404correlation vs. covariancefor influence functions 250–251in discriminant analysis 204in factor analysis 156in PCA 7, 10, 14, 21–26, 40, 42,65, 74, 76, 83, 98, 134see also conditionalcovariance/correlationmatrices, influencefunctions, maximumlikelihood estimation,partial correlation, robustestimationcorrespondence analysis 46, 79,102, 104–107, 338, 341–343,353, 372, 373, 375, 385, 386,391multiple correspondence analysis102, 343, 375, 376Cramér–von Mises statistic 402decomposition into ‘PCs’ 402crime rates 147–149, 300cross-correlation asymmetric PCA401cross-validation 112, 120–127, 131,132, 175, 177, 185, 187, 239,253