Nonparametric shrinkage estimator for covariance matrix under heterogeneity and high dimensions conditions
المؤلف
المصدر
Al Kut Journal of Economic and Administrative Sciences
العدد
المجلد 2018، العدد 29 (30 سبتمبر/أيلول 2018)، ص ص. 19-28، 10ص.
الناشر
جامعة واسط كلية الإدارة و الاقتصاد
تاريخ النشر
2018-09-30
دولة النشر
العراق
عدد الصفحات
10
التخصصات الرئيسية
العلوم الاقتصادية والمالية وإدارة الأعمال
الموضوعات
الملخص EN
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 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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Salih, Ahmad Mahdi. 2018. Nonparametric shrinkage estimator for covariance matrix under heterogeneity and high dimensions conditions. Al Kut Journal of Economic and Administrative Sciences،Vol. 2018, no. 29, pp.19-28.
https://search.emarefa.net/detail/BIM-1206295
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Salih, Ahmad Mahdi. Nonparametric shrinkage estimator for covariance matrix under heterogeneity and high dimensions conditions. Al Kut Journal of Economic and Administrative Sciences Vol. 2019, no. 29 (Sep. 2018), pp.19-28.
https://search.emarefa.net/detail/BIM-1206295
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Salih, Ahmad Mahdi. Nonparametric shrinkage estimator for covariance matrix under heterogeneity and high dimensions conditions. Al Kut Journal of Economic and Administrative Sciences. 2018. Vol. 2018, no. 29, pp.19-28.
https://search.emarefa.net/detail/BIM-1206295
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
-
رقم السجل
BIM-1206295
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر