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Dimension reduction via principal variables

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  • Cumming, J.A.
  • Wooff, D.A.

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  • Cumming, J.A. & Wooff, D.A., 2007. "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 550-565, September.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:550-565
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. W. J. Krzanowski, 1987. "Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(1), pages 22-33, March.
    3. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    4. Friendly M., 2002. "Corrgrams: Exploratory Displays for Correlation Matrices," The American Statistician, American Statistical Association, vol. 56, pages 316-324, November.
    5. I. T. Jolliffe, 1973. "Discarding Variables in a Principal Component Analysis. Ii: Real Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 21-31, March.
    6. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    7. Wayne Velicer, 1976. "Determining the number of components from the matrix of partial correlations," Psychometrika, Springer;The Psychometric Society, vol. 41(3), pages 321-327, September.
    8. J. N. R. Jeffers, 1967. "Two Case Studies in the Application of Principal Component Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 225-236, November.
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    2. James Taylor & Jochen Einbeck, 2013. "Challenging the curse of dimensionality in multivariate local linear regression," Computational Statistics, Springer, vol. 28(3), pages 955-976, June.
    3. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.

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