Robust sparse precision matrix estimation for high-dimensional compositional data

Motivated by the rapid development in the high-dimensional compositional data analysis, an Approximate-Plug framework with theoretical justifications is proposed to provide robust precision matrix estimation for this kind of data under the sparsity assumption. To be specific, we first construct a Huber-robustness estimator ((gamma) over tilde)& nbsp;to approximate the centered log-ratio covariance matrix. Then we plug ((gamma) over tilde) into a constrained l1-minimization procedure to obtain the final estimator tilde ((omega) over tilde). Through imposing some mild conditions, we derive the convergence rate under the entrywise maximum norm and the spectral norm. Given that SpiecEasi in Kurtz et al. (2015) shares same routine with us but lacks of robustness and theoretical guarantees, simulation studies are conducted to show the privileges of our procedure. We also apply the proposed method on a real data. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Motivated by the rapid development in high-dimensional compositional data analysis, this paper proposes an Approximate-Plug framework with theoretical justifications for robust precision matrix estimation under the sparsity assumption. The proposed method constructs a robust estimator and utilizes a constrained minimization procedure to obtain the final estimator. Simulation studies and real data application demonstrate the superiority of the proposed method over existing approaches.