Lower bound estimation for a family of high-dimensional sparse covariance matrices

Abstract

Lower bound estimation plays an important role for establishing the minimax risk. A key step in lower bound estimation is deriving a lower bound of the affinity between two probability measures. This paper provides a simple method to estimate the affinity between mixture probability measures. Then we apply the lower bound of the affinity to establish the minimax lower bound for a family of sparse covariance matrices, which contains Cai–Ren–Zhou’s theorem in [T. Cai, Z. Ren and H. Zhou, Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation, Electron. J. Stat. 10(1) (2016) 1–59] as a special example.