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Ensemble sparse estimation of covariance structure for exploring genetic disease data
Computational Statistics & Data Analysis ( IF 1.8 ) Pub Date : 2021-03-15 , DOI: 10.1016/j.csda.2021.107220
Xiaoning Kang , Mingqiu Wang

High-dimensional data often occur nowadays in various areas, such as genetic and microarray data. The covariance matrix is of fundamental importance in analyzing the relationship between multivariate variables. A powerful tool for estimating a covariance matrix is the modified Cholesky decomposition, which allows for unconstrained estimation and guarantees the positive definiteness of the estimate. However, it requires a pre-specified ordering of variables before analysis, which is often not available in the real data. Hence, an ensemble Cholesky-based sparse estimation is proposed for a high-dimensional covariance matrix by adopting the model averaging idea. The asymptotically theoretical convergence rate is established under some regularity conditions. The merits of the proposed model are illustrated by the numerical study and two genetic disease data.



中文翻译:

用于探索遗传疾病数据的协方差结构的集合稀疏估计

如今,高维数据经常出现在各个领域,例如遗传和微阵列数据。协方差矩阵对于分析多元变量之间的关系至关重要。估计协方差矩阵的强大工具是修正的Cholesky分解,它允许无限制的估计并保证估计的正定性。但是,它需要在分析之前对变量进行预先指定的排序,而这在实际数据中通常是不可用的。因此,通过采用模型平均的思想,提出了一种基于整体Cholesky的稀疏估计方法,用于高维协方差矩阵。在某些规律性条件下建立了渐近理论收敛速度。

更新日期:2021-03-22
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