TY - JOUR ID - TI - Nonparametric Shrinkage Estimator for Covariance Matrix Under Heterogeneity and High Dimensions Conditions AU - Ahmed Mahdi Salih PY - 2018 VL - 1 IS - 29 SP - 19 EP - 28 JO - Al Kut Journal of Economics and Administrative Sciences مجلة الكوت للعلوم الاقتصادية والادارية SN - 1999558X 27074560 AB - 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.

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