18 ноября (пятница), 17:00, ауд.616 ИППИ РАН.
Денис Беломестный (University of Duisburg-Essen)
Threshold estimation for sparse high-dimensional deconvolution
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 (logp)/n with n being the sample size.
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| 15.11.2016 | |