The problem of estimating the large covariance matrix of both normal and non-normal distributions is addressed. In convex combinations of the sample covariancea€€matrix and the identity matrix multiplied by a scalor statistic, we suggest a newa€€estimator of the optimal weight based on exact or approximately unbiased estimators of the numerator and denominator of the optimal weight in non-normal cases.a€€a€€It is also demonstrated that the estimators given in the literature have second-order biases. It is numerically shown that the proposed estimator has a good riska€€performance. --
['Yuki Ikeda', 'Tatsuya Kubokawa', 'Muni S. Srivastava']
Comparison of Linear Shrinkage Estimators of a Large Covariance Matrix in Normal and Non-normal Distributions