Bayesian adaptive Lasso estimation of large graphical model based on modified Cholesky decomposition

被引：0
|
作者：
Li, Fanqun [1 ]
Zhao, Mingtao [1 ]
Zhang, Kongsheng [1 ]
机构：
[1] Anhui Univ Finance & Econ, Inst Stat & Appl Math, Bengbu 233000, Peoples R China
来源：
STATISTICS & PROBABILITY LETTERS | 2024年 / 206卷
关键词：
Graphical model; Regression; Bayesian adaptive Lasso; Modified Cholesky decomposition; SELECTION; REGRESSION;
D O I：
10.1016/j.spl.2023.110004
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, based on the modified Cholesky decomposition of the precision matrix, we propose Bayesian adaptive Lasso estimation and maximum adaptive posterior estimation for graphical model. We also recover the graph by minimizing the decoupled shrinkage and selection loss function.
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页数：7
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