A Constrained l_{1} Minimization Approach to Sparse Precision Matrix Estimation
Tony Cai, , Weidong Liu, and Xi Luo
To get the clime estimator, use function
sugm(data, lambda = NULL, nlambda = NULL, lambda.min.ratio = NULL,
rho = NULL, method = "clime", sym = "or", shrink=NULL,
prec = 1e-4, max.ite = 1e4, standardize = FALSE,
perturb = TRUE, verbose = TRUE)
Cai, T., Zhang, C.-H. & Zhou, H. (2010).
Optimal rates of convergence for covariance matrix estimation
The Annals of Statistics 38, 2118-2144.
Cai, T., Wang, L. & Xu, G. (2010).
Shifting inequality and recovery of sparse signals
IEEE Transactions on Signal Processing 58, 1300-1308.
Cai, T. & Zhou, H. (2012).
Optimal rates of convergence for sparse covariance matrix estimation.
The Annals of Statistics 40, 2389-2420.
Cai, T. & Zhou, H. (2012).
Minimax estimation of large covariance matrices under l_{1} norm (with discussion)
Statistica Sinica 22, 1319-1378.
Cai, T., Liu, W. & Zhou, H. (2014).
Estimating sparse precision matrix: Optimal rates of convergence and adaptive estimation.
The Annals of Statistics, to appear.