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学术报告:High-dimensional Correlation Matrix Estimation for General Continuous Data With Bagging Technique

发布时间:2019年04月02日 浏览次数:发布者:tymath2

      SpeakerDr.Chaojie Wang

                       Jiangsu University

Title: High-dimensional Correlation Matrix Estimation for General Continuous Data With Bagging Technique

Time13 April 2019,16:30

Location海韵实验楼108

Abstract: High-dimensional covriance matrix estimation plays a central role in multivariate statistical analysis. It is well-known that the sample covariance matrix is singular when p>n, while the covariance estimator must be positive-definite. This motivates some modifications of the sample estimator when considering to remain its nice properties on each entry. In this paper, we modify the sample correlation matrix by using Bagging (Bootstrap Aggregating) technique. The proposed estimator inherits nice properties, e.g., unbiased and efficient, of the sample estimator, and it is flexible for general continuous data. Under few prior assumptions, we show that the estimator can remain positive-definite with high probability in finite samples theoretically. Simulation results and a real application are implemented to demonstrate our method are competitive with other estimators.

      Speaker Introduction:王超杰博士2014年本科毕业于浙江大学数学系,2018年于香港中文大学统计系获得博士学位,现任职于江苏大学理学院金融数学系。研究方向为协方差矩阵估计,贝叶斯统计,金融量化模型等。

 

 

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