Revisiting the predictive power of kernel principal components

Ben Jones and Andreas Artemiou

Statistics & Probability Letters, 2021, vol. 171, issue C

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.

Keywords: Model-free regression; Conditional independence; Hilbert spaces; Dimension reduction; Principal components (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spl.2020.109019

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