Bayesian Lasso with neighborhood regression method for Gaussian graphical model

被引：0
|
作者：
Fan-qun Li
Xin-sheng Zhang
机构：
[1] Fudan University,Department of Statistics, School of Management
[2] Anhui University of Finance and Economics,Institute of Statistics and Applied Mathematics
来源：
Acta Mathematicae Applicatae Sinica, English Series | 2017年 / 33卷
关键词：
gaussian graphical model; regression; precision matrix; Bayesian Lasso; Frobenius loss; 62F15; 62H12;
D O I：
暂无
中图分类号：
学科分类号：
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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收藏
页码：485 / 496
页数：11
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