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)
ReferencesAguilera, A.M., Gutiérrez, R., Ocaña, F.A. and Valderrama, M.J. (1995).Computational approaches to estimation in the principal componentanalysis of a stochastic process. Appl. Stoch. Models Data Anal., 11,279–299.Aguilera, A.M., Ocaña, F.A. and Valderrama, M.J. (1997). An approximatedprincipal component prediction model for continuous timestochastic processes. Appl. Stoch. Models Data Anal., 13, 61–72.Aguilera, A.M., Ocaña, F.A. and Valderrama, M.J. (1999a). Forecastingwith unequally spaced data by a functional principal component analysis.Test, 8, 233–253.Aguilera, A.M., Ocaña, F.A. and Valderrama, M.J. (1999b). Forecastingtime series by functional PCA. Discussion of several weighted approaches.Computat. Statist., 14, 443–467.Ahamad, B. (1967). An analysis of crimes by the method of principalcomponents. Appl. Statist., 16, 17–35.Aires, F., Chedin, A. and Nadal, J.P. (2000). Independent component analysisof multivariate time series: Application to tropical SST variability.J. Geophys. Res.—Atmos., 105 (D13), 17,437–17,455.Aitchison, J. (1982). The statistical analysis of compositional data (withdiscussion). J. R. Statist. Soc. B, 44, 139–177.Aitchison, J. (1983). Principal component analysis of compositional data.Biometrika, 70, 57–65.Aitchison, J. (1986). The Statistical Analysis of Compositional Data.London: Chapman and Hall.
416 ReferencesAkaike, H. (1974). A new look at the statistical model identification. IEEETrans. Autom. Cont., 19, 716–723.Aldenderfer, M.S. and Blashfield, R.K. (1984). Cluster Analysis. BeverlyHills: Sage.Aldrin, M. (2000). Multivariate prediction using softly shrunk reduced-rankregression. Amer. Statistician, 54, 29–34.Ali, A., Clarke, G.M. and Trustrum, K. (1985). Principal component analysisapplied to some data from fruit nutrition experiments. Statistician,34, 365–369.Al-Kandari, N. (1998). Variable Selection and Interpretation in PrincipalComponent Analysis. Unpublished Ph.D. thesis, University of Aberdeen.Al-Kandari, N.M. and Jolliffe, I.T. (2001). Variable selection and interpretationof covariance principal components. Commun. Statist.—Simul.Computat., 30, 339-354.Allan, R., Chambers, D., Drosdowsky, W., Hendon, H., Latif, M., Nicholls,N., Smith, I., Stone, R. and Tourre, Y. (2001). Is there an Indian Oceandipole, and is it independent of the El Niño—Southern Oscillation?CLIVAR Exchanges, 6, 18–22.Allen, D.M. (1974). The relationship between variable selection and dataaugmentation and a method for prediction. Technometrics, 16, 125–127.Allen M.R. and Robertson, A.W. (1996). Distinguishing modulated oscillationsfrom coloured noise in multivariate datasets. Climate Dynam., 12,775–784.Allen M.R. and Smith, L.A. (1996). Monte Carlo SSA: Detecting irregularoscillations in the presence of colored noise. J. Climate, 9, 3373–3404.Allen M.R. and Smith, L.A. (1997). Optimal filtering in singular spectrumanalysis. Phys. Lett. A, 234, 419–428.Allen, M.R. and Tett, S.F.B. (1999). Checking for model consistency inoptimal fingerprinting. Climate Dynam., 15, 419–434.Ambaum, M.H.P., Hoskins, B.J. and Stephenson, D.B. (2001). Arcticoscillation or North Atlantic Oscillation. J. Climate, 14, 3495–3507.Anderson, A.B., Basilevsky, A. and Hum, D.P.J. (1983). Missing data: Areview of the literature. In Handbook of Survey Research, eds. P.H. Rossi,J.D. Wright and A.B. Anderson, 415–494.Anderson, T.W. (1957). Maximum likelihood estimates for a multivariatenormal distribution when some observations are missing. J. Amer.Statist. Assoc., 52, 200–203.Anderson, T.W. (1963). Asymptotic theory for principal componentanalysis. Ann. Math. Statist., 34, 122–148.Anderson, T.W. (1984). Estimating linear statistical relationships. Ann.Statist., 12, 1–45.Andrews, D.F. (1972). Plots of high-dimensional data. Biometrics, 28, 125–136.
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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
- 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 470 and 471: References 439Kazi-Aoual, F., Sabat
- 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
416 ReferencesAkaike, H. (1974). A new look at the statistical model identification. IEEETrans. Autom. Cont., 19, 716–723.Aldenderfer, M.S. and Blashfield, R.K. (1984). Cluster <strong>Analysis</strong>. BeverlyHills: Sage.Aldrin, M. (2000). Multivariate prediction using softly shrunk reduced-rankregression. Amer. Statistician, 54, 29–34.Ali, A., Clarke, G.M. and Trustrum, K. (1985). <strong>Principal</strong> component analysisapplied to some data from fruit nutrition experiments. Statistician,34, 365–369.Al-Kandari, N. (1998). Variable Selection and Interpretation in <strong>Principal</strong><strong>Component</strong> <strong>Analysis</strong>. Unpublished Ph.D. thesis, University of Aberdeen.Al-Kandari, N.M. and <strong>Jolliffe</strong>, I.T. (2001). Variable selection and interpretationof covariance principal components. Commun. Statist.—Simul.Computat., 30, 339-354.Allan, R., Chambers, D., Drosdowsky, W., Hendon, H., Latif, M., Nicholls,N., Smith, I., Stone, R. and Tourre, Y. (2001). Is there an Indian Oceandipole, and is it independent of the El Niño—Southern Oscillation?CLIVAR Exchanges, 6, 18–22.Allen, D.M. (1974). The relationship between variable selection and dataaugmentation and a method for prediction. Technometrics, 16, 125–127.Allen M.R. and Robertson, A.W. (1996). Distinguishing modulated oscillationsfrom coloured noise in multivariate datasets. Climate Dynam., 12,775–784.Allen M.R. and Smith, L.A. (1996). Monte Carlo SSA: Detecting irregularoscillations in the presence of colored noise. J. Climate, 9, 3373–3404.Allen M.R. and Smith, L.A. (1997). Optimal filtering in singular spectrumanalysis. Phys. Lett. A, 234, 419–428.Allen, M.R. and Tett, S.F.B. (1999). Checking for model consistency inoptimal fingerprinting. Climate Dynam., 15, 419–434.Ambaum, M.H.P., Hoskins, B.J. and Stephenson, D.B. (2001). Arcticoscillation or North Atlantic Oscillation. J. Climate, 14, 3495–3507.Anderson, A.B., Basilevsky, A. and Hum, D.P.J. (1983). Missing data: Areview of the literature. In Handbook of Survey Research, eds. P.H. Rossi,J.D. Wright and A.B. Anderson, 415–494.Anderson, T.W. (1957). Maximum likelihood estimates for a multivariatenormal distribution when some observations are missing. J. Amer.Statist. Assoc., 52, 200–203.Anderson, T.W. (1963). Asymptotic theory for principal componentanalysis. Ann. Math. Statist., 34, 122–148.Anderson, T.W. (1984). Estimating linear statistical relationships. Ann.Statist., 12, 1–45.Andrews, D.F. (1972). Plots of high-dimensional data. Biometrics, 28, 125–136.
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