A regularization method for discrete Fourier polynomials
SPOA VII Proceedings of the seventh Spanish symposium on Orthogonal polynomials and applications
Recent results on the regularization of Fourier polynomials
Applied Mathematics and Computation
Convergence for the regularized inversion of Fourier series
Journal of Computational and Applied Mathematics
Smoothing data with correlated noise via Fourier transform
Mathematics and Computers in Simulation
On different facets of regularization theory
Neural Computation
Wavelet regression estimation in nonparametric mixed effect models
Journal of Multivariate Analysis
Simplified Tikhonov and Fourier regularization methods on a general sideways parabolic equation
Journal of Computational and Applied Mathematics
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The classical smoothing data problem is analyzed in a Sobolev space under the assumption of white noise. A Fourier series method based on regularization endowed with generalized cross validation is considered to approximate the unknown function. This approximation is globally optimal, i.e., the mean integrated squared error reaches the optimal rate in the minimax sense. In this paper the pointwise convergence property is studied. Specifically, it is proved that the smoothed solution is locally convergent but not locally optimal. Examples of functions for which the approximation is subefficient are given. It is shown that optimality and superefficiency are possible when restricting to more regular subspaces of the Sobolev space.