H
Harrison H. Zhou
Researcher at Yale University
Publications - 89
Citations - 4911
Harrison H. Zhou is an academic researcher from Yale University. The author has contributed to research in topics: Minimax & Estimator. The author has an hindex of 34, co-authored 88 publications receiving 4283 citations.
Papers
More filters
Journal ArticleDOI
Optimal rates of convergence for covariance matrix estimation
TL;DR: In this article, the optimal rates of convergence for estimating the covariance matrix under both the operator norm and Frobenius norm were established and the minimax upper bound was obtained by constructing a special class of tapering estimators and by studying their risk properties.
Journal ArticleDOI
Optimal rates of convergence for sparse covariance matrix estimation
T. Tony Cai,Harrison H. Zhou +1 more
TL;DR: In this paper, a rate sharp minimax lower bound for estimating sparse covariance matrices under a range of matrix operator norm and Bregman divergence losses was derived, and a thresholding estimator was shown to attain the optimal rate of convergence under the spectral norm.
Journal ArticleDOI
Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation
TL;DR: Minimax rates of convergence for estimating several classes of structured covariance and precision matrices, including bandable, Toeplitz, and sparse covariance matrices as well as sparse precisionMatrices, are given under the spectral norm loss.
Journal ArticleDOI
Asymptotic normality and optimalities in estimation of large Gaussian graphical models
TL;DR: In this article, a regression approach is proposed to obtain asymptotically efficient estimation of each entry of a precision matrix under a sparseness condition relative to the sample size.
Journal ArticleDOI
Optimal rates of convergence for sparse covariance matrix estimation
T. Tony Cai,Harrison H. Zhou +1 more
TL;DR: This paper develops a lower bound technique that is particularly well suited for treating “two-directional” problems such as estimating sparse covariance matrices and establishes the optimal rate of convergence under a range of matrix operator norm and Bregman divergence losses.