Density deconvolution based on wavelets with bounded supports

    Authors

    M. Pensky

    Comments

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    Abbreviated Journal Title

    Stat. Probab. Lett.

    Keywords

    deconvolution; wavelet; thresholding; NONPARAMETRIC DECONVOLUTION; INVERSE PROBLEMS; OPTIMAL RATES; DECOMPOSITION; CONVERGENCE; Statistics & Probability

    Abstract

    Under the assumption that both convolution densities, g and q, have finite degrees of smoothness, we construct a nonlinear wavelet estimator of the unknown density g based on wavelets with bounded supports. We show that this estimator provides local adaptivity to the unknown smoothness of g and, hence, performs better than the estimator based on Meyer-type wavelets if g has irregular behavior. (C) 2002 Elsevier Science B.V. All rights reserved.

    Journal Title

    Statistics & Probability Letters

    Volume

    56

    Issue/Number

    3

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    261

    Last Page

    269

    WOS Identifier

    WOS:000174700500004

    ISSN

    0167-7152

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