Tikhonov regularization for finitely and infinitely smoothing operators
SIAM Journal on Mathematical Analysis
Statistical inverse estimation in Hilbert scales
SIAM Journal on Applied Mathematics
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
On regularization algorithms in learning theory
Journal of Complexity
On inverse problems with unknown operators
IEEE Transactions on Information Theory
On prediction rate in partial functional linear regression
Journal of Multivariate Analysis
Sparse estimation in functional linear regression
Journal of Multivariate Analysis
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We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows us to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits us to get easily mean squared error of prediction as well as estimators of the derivatives of the regression function. We prove that these estimators are minimax and rates of convergence are given for some particular cases.