Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
This work addresses covariance estimation for high-dimensional statistical analysis, offering an incremental improvement over existing methods with a novel algorithmic approach.
The paper tackles the problem of estimating large covariance matrices in high-dimensional data by proposing positive-definite estimators using generalized nonconvex penalties to reduce bias compared to convex methods, and demonstrates improved performance in simulations and a gene clustering example.
This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. Generally, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this work, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.