Innovated scalable efficient estimation in ultra-large Gaussian graphical models
This work addresses the problem of recovering graphical structures in high-dimensional data for statisticians and data scientists, offering a scalable solution with theoretical guarantees, though it appears incremental by combining existing techniques.
The paper tackles the challenge of estimating large precision matrices for Gaussian graphical models by proposing an innovated scalable efficient estimation (ISEE) method, which transforms the problem into covariance matrix estimation and demonstrates scalability and efficient link strength estimation with significant probability under mild conditions.
Large-scale precision matrix estimation is of fundamental importance yet challenging in many contemporary applications for recovering Gaussian graphical models. In this paper, we suggest a new approach of innovated scalable efficient estimation (ISEE) for estimating large precision matrix. Motivated by the innovated transformation, we convert the original problem into that of large covariance matrix estimation. The suggested method combines the strengths of recent advances in high-dimensional sparse modeling and large covariance matrix estimation. Compared to existing approaches, our method is scalable and can deal with much larger precision matrices with simple tuning. Under mild regularity conditions, we establish that this procedure can recover the underlying graphical structure with significant probability and provide efficient estimation of link strengths. Both computational and theoretical advantages of the procedure are evidenced through simulation and real data examples.