Electronic Journal of Statistics

Anisotropic de-noising in functional deconvolution model with dimension-free convergence rates

Rida Benhaddou, Marianna Pensky, and Dominique Picard

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Abstract

In the present paper we consider the problem of estimating a periodic $(r+1)$-dimensional function $f$ based on observations from its noisy convolution. We construct a wavelet estimator of $f$, derive minimax lower bounds for the $L^{2}$-risk when $f$ belongs to a Besov ball of mixed smoothness and demonstrate that the wavelet estimator is adaptive and asymptotically near-optimal within a logarithmic factor, in a wide range of Besov balls. We prove in particular that choosing this type of mixed smoothness leads to rates of convergence which are free of the “curse of dimensionality” and, hence, are higher than usual convergence rates when $r$ is large.

The problem studied in the paper is motivated by seismic inversion which can be reduced to solution of noisy two-dimensional convolution equations that allow to draw inference on underground layer structures along the chosen profiles. The common practice in seismology is to recover layer structures separately for each profile and then to combine the derived estimates into a two-dimensional function. By studying the two-dimensional version of the model, we demonstrate that this strategy usually leads to estimators which are less accurate than the ones obtained as two-dimensional functional deconvolutions. Indeed, we show that unless the function $f$ is very smooth in the direction of the profiles, very spatially inhomogeneous along the other direction and the number of profiles is very limited, the functional deconvolution solution has a much better precision compared to a combination of $M$ solutions of separate convolution equations. A limited simulation study in the case of $r=1$ confirms theoretical claims of the paper.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 1686-1715.

Dates
First available in Project Euclid: 26 June 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1372251196

Digital Object Identifier
doi:10.1214/13-EJS820

Mathematical Reviews number (MathSciNet)
MR3080407

Zentralblatt MATH identifier
1294.62057

Subjects
Primary: 62G05: Estimation
Secondary: 62G08: Nonparametric regression 62P35: Applications to physics

Keywords
Functional deconvolution minimax convergence rate hyperbolic wavelets seismic inversion

Citation

Benhaddou, Rida; Pensky, Marianna; Picard, Dominique. Anisotropic de-noising in functional deconvolution model with dimension-free convergence rates. Electron. J. Statist. 7 (2013), 1686--1715. doi:10.1214/13-EJS820. https://projecteuclid.org/euclid.ejs/1372251196


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