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 447Pack, P., Jolliffe, I.T. and Morgan, B.J.T. (1988). Influential observations inprincipal component analysis: A case study. J. Appl. Statist., 15, 39–52.Pearce, S.C. and Holland, D.A. (1960). Some applications of multivariatemethods in botany. Appl. Statist., 9, 1–7.Pearson, K. (1901). On lines and planes of closest fit to systems of pointsin space. Phil. Mag. (6), 2, 559–572.Peña, D. and Box, G.E.P. (1987). Identifying a simplifying structure intime series. J. Amer. Statist. Assoc., 82, 836–843.Peña, D. and Yohai, V. (1999). A fast procedure for outlier diagnostics inlarge regression problems. J. Amer. Statist. Assoc., 94, 434–445.Penny, K.I. and Jolliffe, I.T (2001). A comparison of multivariate outlierdetection methods for clinical laboratory safety data. Statistician, 50,295-308.Pla, L. (1991). Determining stratum boundaries with multivariate realdata. Biometrics, 47, 1409–1422.Plaut, G. and Vautard, R. (1994). Spells of low-frequency oscillations andweather regimes in the Northern Hemisphere. J. Atmos. Sci., 51, 210–236.Preisendorfer, R.W. (1981). Principal component analysis and applications.Unpublished lecture notes.Amer. Met. Soc. Workshop on PrincipalComponent Analysis, Monterey.Preisendorfer, R.W. and Mobley, C.D. (1982). Data intercomparisontheory, I-V. NOAA Tech. Memoranda ERL PMEL Nos. 38–42.Preisendorfer, R.W. and Mobley, C.D. (1988). Principal ComponentAnalysis in Meteorology and Oceanography. Amsterdam: Elsevier.Press, S.J. (1972). Applied Multivariate Analysis. New York: Holt, Rinehartand Winston.Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992)Numerical Recipes in C, 2nd edition. Cambridge: Cambridge UniversityPress.Priestley, M.B., Subba Rao, T. and Tong, H. (1974). Applications ofprincipal component analysis and factor analysis in the identificationof multivariable systems. IEEE Trans. Autom. Cont., AC-19,730–734.Qian, G., Gabor, G. and Gupta, R.P. (1994). Principal components selectionby the criterion of the minimum mean difference of complexity. J.Multiv. Anal., 49, 55–75.Radhakrishnan, R. and Kshirsagar, A.M. (1981). Influence functions forcertain parameters in multivariate analysis. Commun. Statist., A10,515–529.Ramsay, J.O. (1996). Principal differential analysis: Data reduction bydifferential operators. J. R. Statist. Soc. B, 58, 495–508.Ramsay, J.O. (2000). Functional components of variation in handwriting.J. Amer. Statist. Assoc., 95, 9–15.
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- 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
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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 464 and 465: References 433Gunst, R.F. and Mason
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- Page 480 and 481: References 449Richman M.B. (1993).
- Page 482 and 483: References 451Soofi, E.S. (1988). P
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- Page 488 and 489: References 457regularities in multi
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References 447Pack, P., <strong>Jolliffe</strong>, I.T. and Morgan, B.J.T. (1988). Influential observations inprincipal component analysis: A case study. J. Appl. Statist., 15, 39–52.Pearce, S.C. and Holland, D.A. (1960). Some applications of multivariatemethods in botany. Appl. Statist., 9, 1–7.Pearson, K. (1901). On lines and planes of closest fit to systems of pointsin space. Phil. Mag. (6), 2, 559–572.Peña, D. and Box, G.E.P. (1987). Identifying a simplifying structure intime series. J. Amer. Statist. Assoc., 82, 836–843.Peña, D. and Yohai, V. (1999). A fast procedure for outlier diagnostics inlarge regression problems. J. Amer. Statist. Assoc., 94, 434–445.Penny, K.I. and <strong>Jolliffe</strong>, I.T (2001). A comparison of multivariate outlierdetection methods for clinical laboratory safety data. Statistician, 50,295-308.Pla, L. (1991). Determining stratum boundaries with multivariate realdata. Biometrics, 47, 1409–1422.Plaut, G. and Vautard, R. (1994). Spells of low-frequency oscillations andweather regimes in the Northern Hemisphere. J. Atmos. Sci., 51, 210–236.Preisendorfer, R.W. (1981). <strong>Principal</strong> component analysis and applications.Unpublished lecture notes.Amer. Met. Soc. Workshop on <strong>Principal</strong><strong>Component</strong> <strong>Analysis</strong>, Monterey.Preisendorfer, R.W. and Mobley, C.D. (1982). Data intercomparisontheory, I-V. NOAA Tech. Memoranda ERL PMEL Nos. 38–42.Preisendorfer, R.W. and Mobley, C.D. (1988). <strong>Principal</strong> <strong>Component</strong><strong>Analysis</strong> in Meteorology and Oceanography. Amsterdam: Elsevier.Press, S.J. (1972). Applied Multivariate <strong>Analysis</strong>. New York: Holt, Rinehartand Winston.Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992)Numerical Recipes in C, 2nd edition. Cambridge: Cambridge UniversityPress.Priestley, M.B., Subba Rao, T. and Tong, H. (1974). Applications ofprincipal component analysis and factor analysis in the identificationof multivariable systems. IEEE Trans. Autom. Cont., AC-19,730–734.Qian, G., Gabor, G. and Gupta, R.P. (1994). <strong>Principal</strong> components selectionby the criterion of the minimum mean difference of complexity. J.Multiv. Anal., 49, 55–75.Radhakrishnan, R. and Kshirsagar, A.M. (1981). Influence functions forcertain parameters in multivariate analysis. Commun. Statist., A10,515–529.Ramsay, J.O. (1996). <strong>Principal</strong> differential analysis: Data reduction bydifferential operators. J. R. Statist. Soc. B, 58, 495–508.Ramsay, J.O. (2000). Functional components of variation in handwriting.J. Amer. Statist. Assoc., 95, 9–15.
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