"Comparison of Linear Shrinkage Estimators of a Large Covariance Matrix in Normal and Non-normal Distributions"
The problem of estimating the large covariance matrix of both normal and non-normal distributions is addressed. In convex combinations of the sample covariance matrix and the identity matrix multiplied by a scalor statistic, we suggest a new 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.  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 risk performance. --
Year of publication: |
2015-03
|
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Authors: | Ikeda, Yuki ; Kubokawa, Tatsuya ; Srivastava, Muni S. |
Institutions: | Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics |
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freely available
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