L2-perturbation theory transfers sup-norm rates and weak convergence from covariance kernel estimators to functional principal components, yielding optimal rates, minimax bounds, and asymptotic normality in discrete fixed-design models with errors.
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Transferring supremum-norm rates and weak convergence of covariance kernel estimators to functional principal components
L2-perturbation theory transfers sup-norm rates and weak convergence from covariance kernel estimators to functional principal components, yielding optimal rates, minimax bounds, and asymptotic normality in discrete fixed-design models with errors.