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Multivariate Prediction with Nonlinear Principal Components Analysis: Theory

Author

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  • JOHN GOWER
  • JÖRG BLASIUS

Abstract

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Suggested Citation

  • John Gower & Jörg Blasius, 2005. "Multivariate Prediction with Nonlinear Principal Components Analysis: Theory," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(4), pages 359-372, August.
    • Handle: RePEc:spr:qualqt:v:39:y:2005:i:4:p:359-372
    DOI: 10.1007/s11135-005-3005-1
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    Citations

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    Cited by:

    1. Blasius, Jörg & Eilers, Paul H.C. & Gower, John, 2009. "Better biplots," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3145-3158, June.
    2. Massimiliano Giacalone & Demetrio Panarello & Raffaele Mattera, 2018. "Multicollinearity in regression: an efficiency comparison between Lp-norm and least squares estimators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1831-1859, July.
    3. Jörg Blasius & John Gower, 2005. "Multivariate Prediction with Nonlinear Principal Components Analysis: Application," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(4), pages 373-390, August.
    4. Bastiaan Bruinsma & Marlene Mußotter, 2023. "A Move Forward: Exploring National Identity Through Non-linear Principal Component Analysis in Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 885-903, February.
    5. Bruno Ricca & Massimiliano Ferrara & Salvatore Loprevite, 2023. "Searching for an effective accounting-based score of firm performance: a comparative study between different synthesis techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3575-3602, August.

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