Model selection for inferring Gaussian graphical models

被引：0

作者：

De Canditiis, Daniela ^{[1
]}

Cirulli, Silvia ^{[2
]}

机构：

[1] Inst Applicat Calculus CNR Rome, Rome, Italy

[2] Univ Roma Tor Vergata, Rome, Italy

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2023年 / 52卷 / 12期

关键词：

Gaussian graphical models; grouped Lasso; model selection;

D O I：

10.1080/03610918.2021.2007398

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 article, we deal with the model selection problem for estimating a Gaussian Graphical Model (GGM) by regression based techniques. In fact, although regression based techniques are well understood and have good theoretical properties, it is still not clear which criterion is more appropriate for model selection. In this work we do a comparative study between CV and BIC, obtaining important conclusions that can be of practical interest in different contexts of data analysis.

引用

页码：6084 / 6095

页数：12

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