The Annals of Statistics
- Ann. Statist.
- Volume 44, Number 5 (2016), 2098-2126.
Innovated scalable efficient estimation in ultra-large Gaussian graphical models
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.
Ann. Statist., Volume 44, Number 5 (2016), 2098-2126.
Received: May 2015
Revised: November 2015
First available in Project Euclid: 12 September 2016
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Fan, Yingying; Lv, Jinchi. Innovated scalable efficient estimation in ultra-large Gaussian graphical models. Ann. Statist. 44 (2016), no. 5, 2098--2126. doi:10.1214/15-AOS1416. https://projecteuclid.org/euclid.aos/1473685270
- Supplement to “Innovated scalable efficient estimation in ultra-large Gaussian graphical models”. Due to space constraints, the proofs of Theorem 3 and Proposition 1 and additional technical details are provided in the Supplementary Material .