Smooth hyperbolic wavelet deconvolution with anisotropic structure

Justin Wishart

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)
    10 Downloads (Pure)


    This paper considers a deconvolution regression problem in a multivariate setting with anisotropic structure and constructs an estimator of the function of interest using the hyperbolic wavelet basis. The deconvolution structure assumed is an anisotropic version of the smooth type (either regular-smooth or super-smooth). The function of interest is assumed to belong to a Besov space with anisotropic smoothness. Global performances of the presented hyperbolic wavelet estimators is measured by obtaining upper bounds on convergence rates in the Lp-risk with 1≤p≤2 and 1≤p<∞ in the regular-smooth and super-smooth cases respectively. The results are compared and contrasted with existing convergence results in the literature.
    Original languageEnglish
    Pages (from-to)1694-1716
    Number of pages23
    JournalElectronic Journal of Statistics
    Issue number1
    Publication statusPublished - 24 Apr 2019

    Bibliographical note

    Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


    • Besov spaces
    • Brownian sheet
    • deconvolution
    • Fourier analysis
    • hyperbolic wavelet analysis
    • anisotropic
    • nonparametric regression
    • Wavelets
    • Hyperbolic wavelet analysis
    • Deconvolution
    • Meyer wavelets
    • Anisotropic


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