Deconvolution of spherical data corrupted with unknown noise

DOI10.1214/23-EJS2106arXiv2203.00654MaRDI QIDQ6392505

Elisabeth Gassiat, Jérémie Capitao-Miniconi

Publication date: 1 March 2022

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 dimension $d$. 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.

Mathematics Subject Classification ID

Statistics (62-XX)

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