可列斯基分解
最小度算法
协方差矩阵
协方差
计算机科学
肯定性
数学
算法
协方差矩阵的估计
依赖关系(UML)
数学优化
不完全Cholesky因式分解
正定矩阵
统计
人工智能
特征向量
物理
量子力学
作者
Xiaoning Kang,Zhiyang Zhang,Xinwei Deng
出处
期刊:Springer handbooks
日期:2023-01-01
卷期号:: 887-900
标识
DOI:10.1007/978-1-4471-7503-2_43
摘要
In many engineering applications, estimation of covariance and precision matrices is of great importance, helping researchers understand the dependency and conditional dependency between variables of interest. Among various matrix estimation methods, the modified Cholesky decomposition is a commonly used technique. It has the advantage of transforming the matrix estimation task into solving a sequence of regression models. Moreover, the sparsity on the regression coefficients implies certain sparse structure on the covariance and precision matrices. In this chapter, we first overview the Cholesky-based covariance and precision matrices estimation. It is known that the Cholesky-based matrix estimation depends on a prespecified ordering of variables, which is often not available in practice. To address this issue, we then introduce several techniques to enhance the Cholesky-based estimation of covariance and precision matrices. These approaches are able to ensure the positive definiteness of the matrix estimate and applicable in general situations without specifying the ordering of variables. The advantage of Cholesky-based estimation is illustrated by numerical studies and several real-case applications.
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