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

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References 439Kazi-Aoual, F., Sabatier, R. and Lebreton, J.-D. (1995). Approximationof permutation tests for multivariate inference—application to speciesenvironment relationships. In Data Science and Its Application, eds. Y.Escoufier, B. Fichet, E. Diday, L. Lebart, C. Hayashi, N. Ohsumi and Y.Baba, 51–62. Tokyo: Academic Press.Kazmierczak, J.B. (1985). Analyse logarithmique deux exemples d’application.Rev. Statistique Appliquée, 33, 13–24.Kendall, D.G. (1984). Shape-manifolds, procrustean matrices and complexprojective spaces. Bull. Lond. Math. Soc., 16, 81–121.Kendall, M.G. (1957). A Course in Multivariate Analysis. London: Griffin.Kendall, M.G. (1966). Discrimination and classification. In MultivariateAnalysis, ed. P. R. Krishnaiah, 165–185. New York: Academic Press.Kendall, M.G. and Stuart, A. (1979). The Advanced Theory of Statistics,Vol. 2, 4th edition. London: Griffin.Kent, J.T. (1994). The complex Bingham distribution and shape analysis.J. R. Statist. Soc. B, 56, 285–299.Keramidas, E.M., Devlin, S.J. and Gnanadesikan, R. (1987). A graphicalprocedure for comparing the principal components of several covariancematrices. Commun. Statist.-Simul., 16, 161–191.Kiers, H.A.L. (1993). A comparison of techniques for finding componentswith simple structure. In Multivariate Analysis: Future Directions 2, eds.C.M. Cuadras and C.R. Rao, 67–86. Amsterdam: North-Holland.Kim, K.-Y. and Wu, Q. (1999). A comparison study of EOF techniques:analysis of nonstationary data with periodic statistics. J. Climate, 12,185–199.King, J.R. and Jackson, D.A. (1999). Variable selection in large environmentaldata sets using principal components analysis. Environmetrics,10, 67–77.Klink K. and Willmott, C.J. (1989). Principal components of the surfacewind field in the United States: A comparison of analyses based uponwind velocity, direction, and speed. Int. J. Climatol., 9, 293–308.Kloek, T. and Mennes, L.B.M. (1960). Simultaneous equations estimationbased on principal components of predetermined variables.Econometrica, 28, 45–61.Kneip, A. (1994). Nonparametric estimation of common regressors forsimilar curve data. Ann. Statist., 22, 1386–1427.Kneip, A. and Utikal, K.J. (2001). Inference for density families usingfunctional principal component analysis. J. Amer. Statist. Assoc., 96,519–542 (including discussion).Konishi, S. and Rao, C.R. (1992). Principal component analysis formultivariate familial data. Biometrika, 79, 631–641.Kooperberg, C. and O’Sullivan, F. (1994). The use of a statistical forecastcriterion to evaluate alternative empirical spatial oscillation patterndecomposition methods in climatological fields. Technical report 276,Department of Statistics, University of Washington, Seattle.
440 ReferencesKooperberg, C. and O’Sullivan, F. (1996). Predictive oscillation patterns:A synthesis of methods for spatial-temporal decomposition of randomfields. J. Amer. Statist. Assoc., 91, 1485–1496.Korhonen, P.J. (1984). Subjective principal component analysis. Computat.Statist. Data Anal., 2, 243–255.Korhonen, P. and Siljamäki, A. (1998). Ordinal principal component analysis:Theory and an application. Computat. Statist. Data Anal., 26,411–424.Korth, B. and Tucker, L.R. (1975). The distribution of chance congruencecoefficients from simulated data. Psychometrika, 40, 361–372.Kramer, M.A. (1991). Nonlinear principal component analysis usingautoassociative neural networks. AIChE J., 37, 233–243.Kroonenberg, P.M. (1983a). Three-Mode Principal Component Analysis.Leiden: DSWO Press.Kroonenberg, P.M. (1983b). Annotated bibliography of three-mode factoranalysis. Brit. J. Math. Statist. Psychol., 36, 81–113.Kroonenberg, P.M., Harch, B.D., Basford, K.E. and Cruickshank, A.(1997). Combined analysis of categorical and numerical descriptors ofAustralian groundnut accessions using nonlinear principal componentanalysis. J. Agri. Biol. Environ. Statist., 2, 294–312.Kruskal, J.B. (1964a). Multidimensional scaling by optimizing goodness offit to a nonmetric hypothesis. Psychometrika, 29, 1–27.Kruskal, J.B. (1964b). Nonmetric multidimensional scaling: A numericalmethod. Psychometrika, 29, 115–129.Krzanowski, W.J. (1979a). Some exact percentage points of a statisticuseful in analysis of variance and principal component analysis.Technometrics, 21, 261–263.Krzanowski, W.J. (1979b). Between-groups comparison of principal components.J. Amer. Statist. Assoc., 74, 703–707 (correction 76, 1022).Krzanowski, W.J. (1982). Between-group comparison of principal components—somesampling results. J. Statist. Computat. Simul., 15, 141–154.Krzanowski, W.J. (1983). Cross-validatory choice in principal componentanalysis: Some sampling results. J. Statist. Computat. Simul., 18, 299–314.Krzanowski, W.J. (1984a). Principal component analysis in the presence ofgroup structure. Appl. Statist., 33, 164–168.Krzanowski, W.J. (1984b). Sensitivity of principal components. J. R.Statist. Soc. B, 46, 558–563.Krzanowski, W.J. (1987a). Cross-validation in principal component analysis.Biometrics, 43, 575–584.Krzanowski, W.J. (1987b). Selection of variables to preserve multivariatedata structure, using principal components. Appl. Statist., 36, 22–33.Krzanowski, W.J. (1990). Between-group analysis with heterogeneous covariancematrices: The common principal component model. J. Classific.,7, 81–98.
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Principal ComponentAnalysis,Second
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viPreface to the Second Editionerty
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viiiPreface to the Second EditionA
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xPreface to the First Editionand in
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xiiPreface to the First EditionIn m
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xviAcknowledgmentsthese institution
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xviiiContents3.4.1 Example ........
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xxContents10 Outlier Detection, Inf
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xxivList of Figures5.2 Artistic qua
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xxviiiList of Tables6.1 First six e
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2 1. IntroductionFigure 1.1. Plot o
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4 1. IntroductionFigure 1.3. Studen
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1.2. A Brief History of Principal C
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1.2. A Brief History of Principal C
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2.1. Optimal Algebraic Properties o
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2.2. Geometric Properties of Popula
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2.3. Principal Components Using a C
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2.4. Principal Components with Equa
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3Mathematical and StatisticalProper
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where3.1. Optimal Algebraic Propert
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3.2. Geometric Properties of Sample
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3.3. Covariance and Correlation Mat
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3.3. Covariance and Correlation Mat
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3.4. Principal Components with Equa
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show that X = ULA ′ .⎡ULA ′ =
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3.6. Probability Distributions for
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3.7. Inference Based on Sample Prin
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3.7.2 Interval Estimation3.7. Infer
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3.8. Patterned Covariance and Corre
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3.9. Models for Principal Component
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3.9. Models for Principal Component
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4Principal Components as a SmallNum
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4.1. Anatomical Measurements 65Tabl
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4.1. Anatomical Measurements 67spac
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4.2. The Elderly at Home 69Table 4.
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4.3. Spatial and Temporal Variation
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4.3. Spatial and Temporal Variation
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4.4. Properties of Chemical Compoun
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4.5. Stock Market Prices 77Table 4.
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5. Graphical Representation of Data
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Anatomical Measurements5.1. Plottin
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5.1. Plotting Two or Three Principa
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5.2. Principal Coordinate Analysis
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5.3. Biplots 91columns, L is an (r
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5.3. Biplots 93ButandSubstituting i
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5.3. Biplots 95The vector gi ∗ co
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5.3. Biplots 97Figure 5.3. Biplot u
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5.3. Biplots 99Table 5.2. First two
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5.3. Biplots 101Figure 5.5. Biplot
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5.4. Correspondence Analysis 103of
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5.4. Correspondence Analysis 105Fig
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5.6. Displaying Intrinsically High-
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5.6. Displaying Intrinsically High-
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6Choosing a Subset of PrincipalComp
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6.1. How Many Principal Components?
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6.2. Choosing m, the Number of Comp
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6.2. Choosing m, the Number of Comp
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6.3. Selecting a Subset of Variable
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6.4. Examples Illustrating Variable
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7.1. Models for Factor Analysis 151
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7.2. Estimation of the Factor Model
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7.3. Comparisons Between Factor and
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7.4. An Example of Factor Analysis
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7.4. An Example of Factor Analysis
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7.5. Concluding Remarks 165To illus
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8Principal Components in Regression
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8.1. Principal Component Regression
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8.1. Principal Component Regression
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8.2. Selecting Components in Princi
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8.2. Selecting Components in Princi
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8.3. Connections Between PC Regress
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8.4. Variations on Principal Compon
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8.5. Variable Selection in Regressi
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8.5. Variable Selection in Regressi
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8.6. Functional and Structural Rela
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8.7. Examples of Principal Componen
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Table 8.3. Principal component regr
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9Principal Components Used withOthe
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9.1. Discriminant Analysis 201on th
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9.1. Discriminant Analysis 203Figur
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9.1. Discriminant Analysis 205Corbi
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9.1. Discriminant Analysis 207that
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9.1. Discriminant Analysis 209betwe
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9.2. Cluster Analysis 211dimensiona
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9.2. Cluster Analysis 213Before loo
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9.2. Cluster Analysis 215Figure 9.3
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9.2. Cluster Analysis 217demographi
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9.2. Cluster Analysis 219county clu
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9.2. Cluster Analysis 221choosing a
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9.3. Canonical Correlation Analysis
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10.1. Detection of Outliers Using P
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10.2. Influential Observations in a
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10.3. Sensitivity and Stability 259
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10.3. Sensitivity and Stability 261
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10.4. Robust Estimation of Principa
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11Rotation and Interpretation ofPri
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11.1. Rotation of Principal Compone
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oot of the corresponding eigenvalue
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11.2. Alternatives to Rotation 279w
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11.2. Alternatives to Rotation 281F
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11.2. Alternatives to Rotation 283F
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11.2. Alternatives to Rotation 285T
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11.2. Alternatives to Rotation 287T
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11.2. Alternatives to Rotation 289A
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11.2. Alternatives to Rotation 291
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11.3. Simplified Approximations to
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11.3. Simplified Approximations to
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11.4. Physical Interpretation of Pr
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12Principal Component Analysis forT
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12.1. Introduction 301series is alm
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12.2. PCA and Atmospheric Time Seri
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and a typical row of the matrix is1
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12.3. Functional PCA 317A key refer
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12.3. Functional PCA 319The sample
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12.3. Functional PCA 321speed (mete
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12.3. Functional PCA 323of the data
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12.3. Functional PCA 325subject to
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12.3. Functional PCA 327series than
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12.4. PCA and Non-Independent Data
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13.1. Principal Component Analysis
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13.2. Analysis of Size and Shape 34
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13.2. Analysis of Size and Shape 34
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13.5. Common Principal Components 3
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13.7. PCA in Statistical Process Co
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13.8. Some Other Types of Data 369A
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13.8. Some Other Types of Data 371d
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14Generalizations and Adaptations o
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14.1. Non-Linear Extensions of Prin
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14.1. Additive Principal Components
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14.2. Weights, Metrics, Transformat
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Page 424 and 425: 14.3. PCs in the Presence of Second
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Page 432 and 433: 14.6. Miscellanea 401• Linear App
Page 434 and 435: 14.6. Miscellanea 40314.6.3 Regress
Page 436 and 437: 14.7. Concluding Remarks 405space o
Page 438 and 439: Appendix AComputation of Principal
Page 440 and 441: A.1. Numerical Calculation of Princ
Page 446 and 447: ReferencesAguilera, A.M., Gutiérre
Page 448 and 449: References 417Apley, D.W. and Shi,
Page 450 and 451: References 419Benasseni, J. (1986b)
Page 452 and 453: References 421Boik, R.J. (1986). Te
Page 454 and 455: References 423Castro, P.E., Lawton,
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
Page 462 and 463: References 431in Descriptive Multiv
Page 464 and 465: References 433Gunst, R.F. and Mason
Page 466 and 467: References 435Hocking, R.R., Speed,
Page 468 and 469: References 437Jeffers, J.N.R. (1978
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
Page 478 and 479: References 447Pack, P., Jolliffe, I
Page 480 and 481: References 449Richman M.B. (1993).
Page 482 and 483: References 451Soofi, E.S. (1988). P
Page 484 and 485: References 453Tenenbaum, J.B., de S
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
Page 498 and 499: Index 467see also hypothesis testin
Page 500 and 501: Index 469PC algorithms with noise 4
Page 502 and 503: Index 471in functional and structur
Page 504 and 505: Index 473variance ellipsoids,S-esti
Page 506 and 507: Index 475spatial correlation/covari
Page 508 and 509: Index 477for covariance matrices 26
Page 510 and 511: Author Index 479Belsley, D.A. 169Be
Page 512 and 513: Author Index 481Fowlkes, E.B. 377Fr
Page 514 and 515: Author Index 483Krzanowski, W.J. 46
Page 516 and 517: Author Index 485Rencher, A.C. 64, 1
Page 518: Author Index 487Yaguchi, H. 371Yana
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