The estimation of a large covariance matrix is challenging when the dimension p relative to sample size n. Common approaches deal with challenge have been based on thresholding or shrinkage methods in estimating matrices. However, many applications (e.g., regression, forecast combination, portfolio selection), what we need not but its inverse (the precision matrix). In this paper introduce meth...