Efficient estimation of covariance selection models

被引:121
作者
Wong, F [1 ]
Carter, CK
Kohn, R
机构
[1] Univ New S Wales, Australian Grad Sch Management, Sydney, NSW 2052, Australia
[2] CSIRO, Math & Informat Sci, Sydney, NSW, Australia
[3] Univ New S Wales, Sch Econ, Fac Commerce & Econ, Sydney, NSW 2052, Australia
关键词
Bayesian estimation; Gaussian graphical model; model averaging; multivariate regression; partial correlation;
D O I
10.1093/biomet/90.4.809
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
A Bayesian method is proposed for estimating an inverse covariance matrix from Gaussian data. The method is based on a prior that allows the off-diagonal elements of the inverse covariance matrix to be zero, and in many applications results in a parsimonious parameterisation of the covariance matrix. No assumption is made about the structure of the corresponding graphical model, so the method applies to both nondecomposable and decomposable graphs. All the parameters are estimated by model averaging using an efficient Metropolis-Hastings sampling scheme. A simulation study demonstrates that the method produces statistically efficient estimators of the covariance matrix, when the inverse covariance matrix is sparse. The methodology is illustrated by applying it to three examples that are high-dimensional relative to the sample size.
引用
收藏
页码:809 / 830
页数:22
相关论文
共 26 条