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

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

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

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476 Indexsupervised/unsupervised learning200SVD analysis, see maximumcovariance analysisSVD see singular valuedecompositionsweep-out components 403switching of components 259t-distribution/t-tests 186, 187, 191,193, 196, 197, 204, 205multivariate t-distribution 264,364T -mode analysis 308, 398temperatures 22, 274, 316, 332air temperatures 71, 211, 302,303, 329sea-surface temperatures 73,211, 274, 275, 278–283, 286,289, 310–314, 364, 396tensor-based PCA 398three-mode factor analysis 397three-mode PCA 368, 397, 398time series 49, 56, 72, 74, 76, 128,129, 148, 274, 290, 298–337,360, 365, 369, 370, 384, 393,397, 398, 401co-integration 330distributed lag model 337moving averages 303, 368seasonal dependence 300, 303,314, 315stationarity 300, 303, 304, 314,316, 327, 330tests for randomness (whitenoise) 128see also autocorrelation,autoregressive processes,frequency domain PCs, rednoise, spectral analysis,trend, white noiseTöplitz matrices 56, 303, 304transformed variables 64, 248, 374,376, 377, 382, 386logarithmic transformation 24,248, 344, 345, 347–349, 372,388, 390trend 148, 326, 336removal of trend 76, 393tri-diagonal matrices 410truncation of PC coefficients 67,293–296two-dimensional PC plots 2–4,78–85, 130, 201–203, 212,214–219, 234–236, 242–247,258, 299see also biplots, correspondenceanalysis, interpretationof two-dimensional plots,principal co-ordinateanalysis, projection pursuittwo-stage PCA 209, 223uncentred ‘covariances’ 290, 390uncentred PCA 41, 42, 349, 372,389, 391units of measurement 22, 24, 65,74, 211, 274, 374, 388, 391upper triangular matrices, seelower triangular matricesvariable selection, see selection ofvariablesvariance inflation factors (VIFs),see multicollinearitiesvariances for PCs, see PCvariancesvariation between means 60, 85,96, 158varimax rotation 153, 154,162–165, 182, 188, 191, 238,270, 271, 274, 277–278vector-valued data 129, 369, 370weighted PCA 21, 209, 241, 330,353, 382–385weightsexponentially decreasing 337,368, 384

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