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Structural Learning Seminar
November 18, 2016 17:00–19:00, Moscow, IITP, Bol'shoi Karetnyi per. 19 1
 


Threshold estimation for sparse high-dimensional deconvolution

D. V. Belomestny

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Abstract: The problem of covariance estimation for a p-dimensional normal vector X ∼ N(0, Σ) observed with additional noise is studied. Only a very general non-parametric assumption is imposed on the distribution of the noise. In this semi-parametric deconvolution problem spectral thresholding estimators are constructed that adapt to sparsity in Σ. We prove that the minimax convergence rates logarithmic in log p/n with n being the sample size.

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