Improving Kernel-Based Nonasymptotic Simultaneous Confidence Bands

The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees. The target function is assumed to be band-limited and the approach is based on the theory of Paley-Wiener reproducing kernel Hilbert spaces. The starting poi...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2023-01, Vol.56 (2), p.10357-10362
Main Authors: Csáji, Balázs Csanád, Horváth, Bálint
Format: Article
Language:English
Subjects:
convex optimization
estimation and filtering
nonlinear system identification
nonparametric methods
randomized algorithms
statistical data analysis
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Summary:The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees. The target function is assumed to be band-limited and the approach is based on the theory of Paley-Wiener reproducing kernel Hilbert spaces. The starting point of the paper is a recently developed algorithm to which we propose three types of improvements. First, we relax the assumptions on the noises by replacing the symmetricity assumption with a weaker distributional invariance principle. Then, we propose a more efficient way to estimate the norm of the target function, and finally we enhance the construction of the confidence bands by tightening the constraints of the underlying convex optimization problems. The refinements are also illustrated through numerical experiments.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2023.10.1047