Comparison of Linear Shrinkage Estimators of a Large Covariance Matrix in Normal and Non-normal Distributions

Yuki Ikeda, Tatsuya Kubokawa and Muni S. Srivastava
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Yuki Ikeda: Graduate School of Economics, The University of Tokyo
Tatsuya Kubokawa: Faculty of Economics, The University of Tokyo
Muni S. Srivastava: Department of Statistics, University of Toronto

No CIRJE-F-970, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: 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. --

Pages: 19pages
Date: 2015-03
New Economics Papers: this item is included in nep-ecm
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