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Journal Articles Electronic Journal of Statistics Year : 2023

Deconvolution of spherical data corrupted with unknown noise.

Abstract

We consider the deconvolution problem for densities supported on a (d-1)-dimensional sphere with unknown center and unknown radius, in the situation where the distribution of the noise is unknown and without any other observations. We propose estimators of the radius, of the center, and of the density of the signal on the sphere that are proved consistent without further information. The estimator of the radius is proved to have almost parametric convergence rate for any dimensiond. When d= 2, the estimator of the density is proved to achieve the same rate of convergence over Sobolev regularity classes of densities as when the noise distribution is known.
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Dates and versions

hal-03584579 , version 1 (23-02-2022)
hal-03584579 , version 2 (07-03-2022)

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Jérémie Capitao-Miniconi, Élisabeth Gassiat. Deconvolution of spherical data corrupted with unknown noise.. Electronic Journal of Statistics , 2023, 17 (1), ⟨10.1214/23-EJS2106⟩. ⟨hal-03584579v2⟩
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