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Revisiting the predictive power of kernel principal components

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  • Jones, Ben
  • Artemiou, Andreas

Abstract

In this short note, recent results on the predictive power of kernel principal component in a regression setting are extended in two ways: (1) in the model-free setting, we relax a conditional independence model assumption to obtain a stronger result; and (2) the model-free setting is also extended in the infinite-dimensional setting.

Suggested Citation

  • Jones, Ben & Artemiou, Andreas, 2021. "Revisiting the predictive power of kernel principal components," Statistics & Probability Letters, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:stapro:v:171:y:2021:i:c:s0167715220303229
    DOI: 10.1016/j.spl.2020.109019
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    References listed on IDEAS

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    1. Artemiou, Andreas & Li, Bing, 2013. "Predictive power of principal components for single-index model and sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 176-184.
    2. Ben Jones & Andreas Artemiou, 2020. "On principal components regression with Hilbertian predictors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 627-644, April.
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