Bayesian Lasso with Neighborhood Regression Method for Gaussian Graphical Model

被引：1
|
作者：
Fan-qun LI [1 ,2 ]
Xin-sheng ZHANG [1 ]
机构：
[1] Department of Statistics, School of Management, Fudan University
[2] Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics
来源：
Acta Mathematicae Applicatae Sinica | 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;
D O I：
暂无
中图分类号：
O212 [数理统计];
学科分类号：
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码：485 / 496
页数：12
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