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题目：Shrinkage estimation of large dimensional precision matrix using random matrix theory
嘉宾：王成（特别研究员） 上海交通大学
时间：2014.12.24（周三）下午4：15
地点：管理科研楼10楼1018会议室
摘要：This paper considers ridge‐type shrinkage estimation of a large dimensional precision matrix. The asymptotic optimal shrinkage coefficients and the theoretical loss are derived.
Data‐driven estimators for the shrinkage coefficients are also conducted based on the asymptotic results from random matrix theory. The new method is distribution‐free and no assumption on the structure of the covariance matrix or the precision matrix is required.
The proposed method also applies to situations where the dimension is larger than the sample size. Numerical studies of simulated and real data demonstrate that the proposed estimator performs better than existing competitors in a wide range of settings.
报告人简历： 2003‐2013 中国科学技术大学 本硕博
2013‐2014 香港浸会大学 博士后
2014至今 上海交通大学数学系 特别研究员
个人主页：http://math.sjtu.edu.cn/faculty/chengwang/
嘉宾：王成（特别研究员） 上海交通大学
时间：2014.12.24（周三）下午4：15
地点：管理科研楼10楼1018会议室
摘要：This paper considers ridge‐type shrinkage estimation of a large dimensional precision matrix. The asymptotic optimal shrinkage coefficients and the theoretical loss are derived.
Data‐driven estimators for the shrinkage coefficients are also conducted based on the asymptotic results from random matrix theory. The new method is distribution‐free and no assumption on the structure of the covariance matrix or the precision matrix is required.
The proposed method also applies to situations where the dimension is larger than the sample size. Numerical studies of simulated and real data demonstrate that the proposed estimator performs better than existing competitors in a wide range of settings.
报告人简历： 2003‐2013 中国科学技术大学 本硕博
2013‐2014 香港浸会大学 博士后
2014至今 上海交通大学数学系 特别研究员
个人主页：http://math.sjtu.edu.cn/faculty/chengwang/
