We present a new method of linear regression based on principal components
using Hilbert-space-valued covariates with unknown reproducing kernels. Nous
develop a computationally efficient approach to estimation and derive
asymptotic theory for the regression parameter estimates under mild
assumptions. We demonstrate the approach in simulation studies as well as in
data analysis using two-dimensional brain images as predictors.

Cet article explore les excursions dans le temps et leurs implications.

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