Abstract
In recent years,the estimate of covariance inverse matrix with high dimemsional which between model fitting and model simplifies .Many scholars have suggested that using maximum penalized likelihood function with L1-norm .The L1-norm can identify sparse signal effectively,but lead to estimator be compressed .To improve this problem,we will find the location of the spares signal and estimate these separately,hoping to reduce the resulting of compression of the estimator .