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In multivariate analysis, the covariance matrix associated with a set of vari-ables of interest (namely response variables) commonly contains valuable infor-mation about the dataset. When the dimensio...
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
Motivated by the latest effort to employ banded matrices to esti-mate a high-dimensional covariance Σ, we propose a test for Σ being banded with possible diverging bandwidth. The test is adaptive to t...
We consider two continuous It\^o semimartingales observed with noise and sampled at stopping times in a nonsynchronous manner. In this article we establish a central limit theorem for the pre-averaged...
We study the minimal sample size N=N(n) that suffices to estimate the covariance matrix of an n-dimensional distribution by the sample covariance matrix in the operator norm, and with an arbitrary fix...
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphi...
We obtain a sharp convergence rate for banded covariance matrix estimates of stationary processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices. We also ...
We provide a characterisation of Gaussian time series which optimise the one-step prediction error subject to the covariance sequence being completely monotone with the first m covariances specified. ...

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