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)
References 453Tenenbaum, J.B., de Silva, V. and Langford, J.C. (2000). A global geometricframework for nonlinear dimensionality reduction. Science, 290,2319–2323.ter Braak, C.J.F. (1983). Principal components biplots and alpha and betadiversity. Ecology, 64, 454–462.ter Braak, C.J.F. and Looman, C.W.N. (1994). Biplots in reduced rankregression. Biom. J., 36, 983–1003.Timmerman, M.E. and Kiers, H.A.L. (2000). Three-mode principal componentsanalysis: Choosing the numbers of components and sensitivityto local optima. Brit. J. Math. Stat. Psychol., 53, 1–16.Thacker, W.C. (1996). Metric-based principal components: Data uncertainties.Tellus, 48A, 584–592.Thacker, W.C. (1999). Principal predictors. Int. J. Climatol., 19, 821–834.Thurstone, L.L. (1931). Multiple factor analysis. Psychol. Rev., 38, 406–427.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J.R. Statist. Soc. B, 58, 267–288.Tipping, M.E. and Bishop, C.M. (1999a). Probabilistic principal componentanalysis. J. R. Statist. Soc. B, 61, 611–622.Tipping, M.E. and Bishop, C.M. (1999b). Mixtures of probabilisticprincipal component analyzers. Neural Computat., 11, 443–482.Titterington, D.M., Smith, A.F.M. and Makov, U.E. (1985). StatisticalAnalysis of Finite Mixture Distributions. New York: Wiley.Townshend, J.R.G. (1984). Agricultural land-cover discrimination usingthematic mapper spectral bands. Int. J. Remote Sensing, 5, 681–698.Torgerson, W.S. (1958). Theory and Methods of Scaling. New York: Wiley.Tortora, R.D. (1980). The effect of a disproportionate stratified design onprincipal component analysis used for variable elimination. Proceedingsof the Amer. Statist. Assoc. Section on Survey Research Methods, 746–750.Treasure, F.P. (1986). The geometry of principal components. Unpublishedessay. University of Cambridge.Trenkler, D. and Trenkler, G. (1984). On the Euclidean distance betweenbiased estimators. Commun. Statist.—Theor. Meth., 13, 273–284.Trenkler, G. (1980). Generalized mean squared error comparisons of biasedregression estimators. Commun. Statist., A9, 1247–1259.Tryon, R.C. (1939). Cluster Analysis. Ann Arbor: Edwards Brothers.Tucker, L.R. (1958). An inter-battery method of factor analysis. Psychometrika,23, 111–136.Tucker, L.R. (1966). Some mathematical notes on three-mode factoranalysis. Psychometrika, 31, 279–311.Tukey, P.A. and Tukey, J.W. (1981). Graphical display of data sets in threeor more dimensions. Three papers in Interpreting Multivariate Data (ed.V. Barnett), 189–275. Chichester: Wiley.
454 ReferencesTurner, N.E. (1998). The effect of common variance and structure patternon random data eigenvalues: implications for the accuracy of parallelanalysis. Educ. Psychol. Meas., 58, 541–568.Uddin, M. (1999). Interpretation of Results from Simplified PrincipalComponents. Unpublished Ph.D. thesis. University of Aberdeen.Underhill, L.G. (1990). The coefficient of variation biplot. J. Classific., 7,241–256.van de Geer, J.P. (1984). Linear relations among k sets of variables.Psychometrika, 49, 79–94.van de Geer, J.P. (1986). Introduction to Linear Multivariate DataAnalysis—Volume 2. Leiden: DSWO Press.van den Brink, P.J. and ter Braak, C.J.F. (1999). Principal responsecurves: Analysis of time-dependent multivariate responses of biologicalcommunity to stress. Environ. Toxicol. Chem., 18, 138–148.van den Dool, H.M., Saha, S. and Johansson, Å. (2000) Empiricalorthogonal teleconnections. J. Climate 13, 1421-1435.van den Wollenberg, A.L. (1977). Redundancy analysis. An alternative forcanonical correlation analysis. Psychometrika, 42, 207–219.van Rijckevorsel, J.L.A. (1988). Fuzzy coding and B-splines. In Componentand Correspondence Analysis. Dimension Reduction by FunctionalApproximation, eds. J.L.A. van Rijckevorsel and J. de Leeuw, 33–54.Chichester: Wiley.Vargas-Guzmán, J.A., Warrick, A.W. and Myers, D.E. (1999). Scale effecton principal component analysis for vector random functions. Math.Geol., 31, 701–722.Vautard, R. (1995). Patterns in time: SSA and MSSA. In Analysis ofClimate Variability: Applications of Statistical Techniques, eds. H. vonStorch and A. Navarra, 259–279. Berlin: Springer.Velicer, W.F. (1976). Determining the number of components from thematrix of partial correlations. Psychometrika, 41, 321–327.Velicer, W.F. and Jackson, D.N. (1990). Component analysis versus commonfactor analysis—some issues in selecting an appropriate procedure.Mult.Behav.Res., 25, 1–28.Verboon, P. (1993). Stability of resistant principal component analysis forqualitative data. In New Directions in Statistical Data Analysis and Robustness,eds. S. Morgenthaler, E. Ronchetti and W. A. Stahel, 265–273.Basel: Birkhäuser.Vermeiren, D., Tavella, D. and Horovitz, A. (2001). Extending principalcomponent analysis to identify portfolio risk contributors. Submitted forpublication.Vigneau, E. and Qannari, E.M. (2001). Clustering of variables aroundlatent components. Submitted for publication.Vines, S.K. (2000). Simple principal components. Appl. Statist., 49, 441–451.
- Page 434 and 435: 14.6. Miscellanea 40314.6.3 Regress
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- Page 446 and 447: ReferencesAguilera, A.M., Gutiérre
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- Page 456 and 457: References 425Cook, R.D. (1986). As
- Page 458 and 459: References 427Dempster, A.P., Laird
- Page 460 and 461: References 429Feeney, G.J. and Hest
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- Page 466 and 467: References 435Hocking, R.R., Speed,
- Page 468 and 469: References 437Jeffers, J.N.R. (1978
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- Page 472 and 473: References 441Krzanowski, W.J. (200
- Page 474 and 475: References 443Mann, M.E. and Park,
- Page 476 and 477: References 445Monahan, A.H., Tangan
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- Page 480 and 481: References 449Richman M.B. (1993).
- Page 482 and 483: References 451Soofi, E.S. (1988). P
- Page 486 and 487: References 455Vong, R., Geladi, P.,
- Page 488 and 489: References 457regularities in multi
- Page 490 and 491: Index 459116, 127-130, 133, 270, 27
- Page 492 and 493: Index 461computationin (PC) regress
- Page 494 and 495: Index 463discriminant principal com
- Page 496 and 497: Index 465of correlations between va
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- Page 500 and 501: Index 469PC algorithms with noise 4
- Page 502 and 503: Index 471in functional and structur
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- Page 510 and 511: Author Index 479Belsley, D.A. 169Be
- Page 512 and 513: Author Index 481Fowlkes, E.B. 377Fr
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- Page 516 and 517: Author Index 485Rencher, A.C. 64, 1
- Page 518: Author Index 487Yaguchi, H. 371Yana
References 453Tenenbaum, J.B., de Silva, V. and Langford, J.C. (2000). A global geometricframework for nonlinear dimensionality reduction. Science, 290,2319–2323.ter Braak, C.J.F. (1983). <strong>Principal</strong> components biplots and alpha and betadiversity. Ecology, 64, 454–462.ter Braak, C.J.F. and Looman, C.W.N. (1994). Biplots in reduced rankregression. Biom. J., 36, 983–1003.Timmerman, M.E. and Kiers, H.A.L. (2000). Three-mode principal componentsanalysis: Choosing the numbers of components and sensitivityto local optima. Brit. J. Math. Stat. Psychol., 53, 1–16.Thacker, W.C. (1996). Metric-based principal components: Data uncertainties.Tellus, 48A, 584–592.Thacker, W.C. (1999). <strong>Principal</strong> predictors. Int. J. Climatol., 19, 821–834.Thurstone, L.L. (1931). Multiple factor analysis. Psychol. Rev., 38, 406–427.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J.R. Statist. Soc. B, 58, 267–288.Tipping, M.E. and Bishop, C.M. (1999a). Probabilistic principal componentanalysis. J. R. Statist. Soc. B, 61, 611–622.Tipping, M.E. and Bishop, C.M. (1999b). Mixtures of probabilisticprincipal component analyzers. Neural Computat., 11, 443–482.Titterington, D.M., Smith, A.F.M. and Makov, U.E. (1985). Statistical<strong>Analysis</strong> of Finite Mixture Distributions. New York: Wiley.Townshend, J.R.G. (1984). Agricultural land-cover discrimination usingthematic mapper spectral bands. Int. J. Remote Sensing, 5, 681–698.Torgerson, W.S. (1958). Theory and Methods of Scaling. New York: Wiley.Tortora, R.D. (1980). The effect of a disproportionate stratified design onprincipal component analysis used for variable elimination. Proceedingsof the Amer. Statist. Assoc. Section on Survey Research Methods, 746–750.Treasure, F.P. (1986). The geometry of principal components. Unpublishedessay. University of Cambridge.Trenkler, D. and Trenkler, G. (1984). On the Euclidean distance betweenbiased estimators. Commun. Statist.—Theor. Meth., 13, 273–284.Trenkler, G. (1980). Generalized mean squared error comparisons of biasedregression estimators. Commun. Statist., A9, 1247–1259.Tryon, R.C. (1939). Cluster <strong>Analysis</strong>. Ann Arbor: Edwards Brothers.Tucker, L.R. (1958). An inter-battery method of factor analysis. Psychometrika,23, 111–136.Tucker, L.R. (1966). Some mathematical notes on three-mode factoranalysis. Psychometrika, 31, 279–311.Tukey, P.A. and Tukey, J.W. (1981). Graphical display of data sets in threeor more dimensions. Three papers in Interpreting Multivariate Data (ed.V. Barnett), 189–275. Chichester: Wiley.
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