康晓宁博士学术报告

    报告题目：An Order-Invariant Sparse Inverse Covariance Matrix Estimation Based on the Modified Cholesky Decomposition

    报 告 人：康晓宁

    主办单位：统计学院

    时    间：2017年5月26日下午16:00-17:00

地    点：科技楼二楼北会议室

Abstract: The modified Cholesky decomposition is commonly used for inverse covariance matrix estimation given a specified order of random variables. However, the order of variables often is not available or cannot be pre-determined. In this work, we propose a novel order-invariant estimator for high-dimensional sparse inverse covariance matrix based on the modified Cholesky decomposition. The proposed method efficiently ensembles a set of estimates obtained from multiple orders of random variables, and by using thresholding technique appropriately it encourages the sparse structure in the estimate. The proposed method not only provides an accurate estimation, but also can effectively capture the underlying structure of the inverse covariance matrix. The consistent property is constructed under some weak regularity conditions. Simulation studies show the superior performance of the proposed method in comparison with other approaches. We also apply the proposed method into the linear discriminant analysis for analyzing classification examples.

康晓宁简介：东北财经大学国际商学院Assistant Professor。2016年7月在弗吉尼亚理工大学获得统计学博士学位。

研究领域包括：Large sparse inverse covariance matrix estimation and its application、High-dimensional data analysis and modeling、Bayesian hierarchical modeling、Semiparametric modeling and inference。