Abstract
This paper deals with the estimation of a function f defined on the sphere \(\mathbb{S}^{d}\) of ℝd+1 from a sample of noisy observation points. We introduce an estimation procedure based on wavelet-like functions on the sphere called needlets and study two estimators f ⊛ and \(f^{\bigstar}\) respectively made adaptive through the use of a stochastic and deterministic needlet-shrinkage method. We show hereafter that these estimators are nearly optimal in the minimax framework, explain why f ⊛ outperforms \(f^{\bigstar}\), and run finite-sample simulations with f ⊛ to demonstrate that our estimation procedure is easy to implement and fares well in practice. We are motivated by applications in geophysical and atmospheric sciences.
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Monnier, JB. Nonparametric regression on the hyper-sphere with uniform design. TEST 20, 412–446 (2011). https://doi.org/10.1007/s11749-011-0233-7
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DOI: https://doi.org/10.1007/s11749-011-0233-7