Sep 27, 2017 · The problem of estimating the covariance matrix \Sigma of a p-variate distribution based on its n observations arises in many data analysis contexts.
Sep 26, 2017 · The problem of estimating the covariance matrix Σ of a p-variate distribution based on its n observations arises in many data analysis contexts.
Sep 27, 2017 · Our analysis exploits the connection between the spectral norm of a Toeplitz matrix and the supremum norm of the corresponding spectral density ...
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The problem of estimating the covariance matrix $\Sigma$ of a $p$-variate distribution based on its $n$ observations arises in many data analysis contexts.
This paper provides a short analysis of the masked sample covariance estimator by means of a matrix concentration inequality. The main result applies to general ...
Missing: Toeplitz | Show results with:Toeplitz
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The main novelty of SSCE in this context is that it predicts the second-order statistics of a masked unknown continuous distribution whereas the former predicts ...
We analyze covariance estimation algorithms under three different cases of imperfect constraints: 1) only rank constraint, 2) both rank and noise power ...
ance, and non-Toeplitz corrupted covariance respectively, using Algorithms 1 and 2. ... Figure 3.4: MSE plots for non-Toeplitz Robust KronPCA estimation of the ...
... Toeplitz covariance matrices. ... masked sample covariance estimator: an analysis using matrix concentration inequalities. ... Toeplitz covariance estimation. In: ...
The masked approach was first introduced in [16] and it allows to describe several regularization techniques such as banding or tapering of the covariance ...