針對共變異數反矩陣在高維度情況下的估計，近年來所提出的方式都是對於配適程度與模型簡化中間做一個折衷的方式，採用L1懲罰項的最大概似估計量，但是儘管L1的限制式能有效的找出稀疏的信號，卻會導致估計量有被壓縮的情況產生；為了改善這個問題，我們將找出稀疏的信號與估計這兩個步驟分開進行，希望能有效降低壓縮所導致的誤差。

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 .

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