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Nonparametric Shrinkage Estimator for Covariance Matrix Under Heterogeneity and High Dimensions Conditions | ||
| Al Kut Journal of Economics Administrative Sciences | ||
| Article 1, Volume 0, Issue 29, 2018, Pages 19-28 | ||
| Author | ||
| Ahmed Mahdi Salih | ||
| Abstract | ||
| In this paper, we discuss different kinds of covariance matrix estimators and their behavior under the conditions of heterogeneity and high dimensions. Covariance matrix estimation that is well-conditioned matrix is very important procedure for many statistical applications which require that. Sometimes, the common estimator of covariance matrix - the sample covariance matrix- suffers from ill conditions and in many cases be invertible and without good qualities of estimator as dimensions of matrix go larger. Here, we view a shrinkage estimator for covariance matrix which is a combination of unbiased estimator and minimum variance estimator with different types of shrinkage factors parametric and non-parametric ones. Simulation study have been made by using Heterogeneous Autoregressive Process ARH(1) as a structure covariance matrix for population, moreover, a comparison has been made among different types of covariance estimators by using minimum mean square errors MMSE. | ||
| Keywords | ||
| nonparametric; Shrinkage; Covariance Matrix | ||
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