Bayesian Lasso with neighborhood regression method for Gaussian graphical model

被引：2
|
作者：
Li, Fan-qun [1 ,2 ]
Zhang, Xin-sheng [1 ]
机构：
[1] Fudan Univ, Sch Management, Dept Stat, Shanghai 200433, Peoples R China
[2] Anhui Univ Finance & Econ, Inst Stat & Appl Math, Bengbu 233000, Peoples R China
来源：
ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES | 2017年 / 33卷 / 02期
基金：
中国国家自然科学基金;
关键词：
gaussian graphical model; regression; precision matrix; Bayesian Lasso; Frobenius loss; NON-DECOMPOSABLE GRAPHS; SELECTION; LIKELIHOOD;
D O I：
10.1007/s10255-017-0676-z
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precision matrix, we propose Bayesian Lasso together with neighborhood regression estimate for Gaussian graphical model. This method can obtain parameter estimation and model selection simultaneously. Moreover, the proposed method can provide symmetric confidence intervals of all entries of the precision matrix.
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页码：485 / 496
页数：12
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