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Density deconvolution based on wavelets with bounded supports

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  • Pensky, Marianna

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.

Suggested Citation

  • Pensky, Marianna, 2002. "Density deconvolution based on wavelets with bounded supports," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 261-269, February.
    • Handle: RePEc:eee:stapro:v:56:y:2002:i:3:p:261-269
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    References listed on IDEAS

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    1. Walter, Gilbert G., 1999. "Density estimation in the presence of noise," Statistics & Probability Letters, Elsevier, vol. 41(3), pages 237-246, February.
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    Cited by:

    1. Liu, Youming & Wu, Cong, 2019. "Point-wise estimation for anisotropic densities," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 112-125.

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    More about this item

    Keywords

    Deconvolution Wavelet Thresholding;

    Statistics

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