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A Cholesky-based sparse covariance estimation with an application to genes data
Journal of Biopharmaceutical Statistics ( IF 1.1 ) Pub Date : 2021-05-29 , DOI: 10.1080/10543406.2021.1931270
Chunshi Li 1 , Mo Yang 2 , Mingqiu Wang 3 , Hong Kang 1 , Xiaoning Kang 4
Affiliation  

ABSTRACT

The modified Cholesky decomposition (MCD) is a powerful tool for estimating a covariance matrix. The regularization can be conveniently imposed on the linear regressions to encourage the sparsity in the estimated covariance matrix to accommodate the high-dimensional data. In this paper, we propose a Cholesky-based sparse ensemble estimate for covariance matrix by averaging a set of Cholesky factor estimates obtained from multiple variable orderings used in the MCD. The sparse estimation is enabled by encouraging the sparsity in the Cholesky factor. The theoretical consistent property is established under some regular conditions. The merits of the proposed method are illustrated through simulation and a maize genes data set.



中文翻译:

应用于基因数据的基于 Cholesky 的稀疏协方差估计

摘要

改进的 Cholesky 分解 (MCD) 是估计协方差矩阵的强大工具。正则化可以方便地应用于线性回归,以鼓励估计协方差矩阵中的稀疏性以适应高维数据。在本文中,我们通过对从 MCD 中使用的多个变量排序获得的一组 Cholesky 因子估计进行平均,提出了一种基于 Cholesky 的协方差矩阵稀疏集成估计。通过鼓励 Cholesky 因子中的稀疏性来启用稀疏估计。理论一致的性质是在一些规则条件下建立的。通过模拟和玉米基因数据集说明了所提出方法的优点。

更新日期:2021-05-29
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