An overview of the estimation of large covariance and precision matrices
An overview of the estimation of large covariance and precision matrices
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DOI：
10.1111/ectj.12061
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发表时间：
2016-02-01
影响因子：
1.9
通讯作者：
Liu, Han
中科院分区：
文献类型：
--
作者：
Fan, Jianqing;Liao, Yuan;Liu, Han
大协方差矩阵和精度矩阵的估计是现代多元分析的基础。然而，在对大型面板经济和金融数据进行统计分析时出现了问题。协方差矩阵揭示了变量之间的边际相关性，而精度矩阵则对给定其余变量的变量对之间的条件相关性进行编码。在本文中，我们提供了一个选择性的审查最近的几个发展估计的大协方差和精度矩阵。我们专注于两个一般的方法：基于排名的方法和基于因素模型的方法。这两种方法的理论和应用。这些方法有望广泛应用于经济和金融数据的分析。
The estimation of large covariance and precision matrices is fundamental in modern multivariate analysis. However, problems arise from the statistical analysis of large panel economic and financial data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on the estimation of large covariance and precision matrices. We focus on two general approaches: a rank-based method and a factor-model-based method. Theories and applications of both approaches are presented. These methods are expected to be widely applicable to the analysis of economic and financial data.