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    Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

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    Type
    Article
    Authors
    Migliorati, Giovanni
    Nobile, Fabio
    Tempone, Raul cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2015-08-28
    Online Publication Date
    2015-08-28
    Print Publication Date
    2015-12
    Permanent link to this record
    http://hdl.handle.net/10754/576075
    
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    Abstract
    We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. The convergence estimates are given in mean-square sense with respect to the sampling measure. The noise may be correlated with the location of the evaluation and may have nonzero mean (offset). We consider both cases of bounded or square-integrable noise / offset. We prove conditions between the number of sampling points and the dimension of the underlying approximation space that ensure a stable and accurate approximation. Particular focus is on deriving estimates in probability within a given confidence level. We analyze how the best approximation error and the noise terms affect the convergence rate and the overall confidence level achieved by the convergence estimate. The proofs of our convergence estimates in probability use arguments from the theory of large deviations to bound the noise term. Finally we address the particular case of multivariate polynomial approximation spaces with any density in the beta family, including uniform and Chebyshev.
    Citation
    Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points 2015 Journal of Multivariate Analysis
    Publisher
    Elsevier BV
    Journal
    Journal of Multivariate Analysis
    DOI
    10.1016/j.jmva.2015.08.009
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0047259X15001931
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jmva.2015.08.009
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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