Innovated scalable efficient inference for ultra-large graphical models

被引：0

作者：

Zhou, Jia ^{[1
]}

Zheng, Zemin ^{[1
]}

Zhou, Huiting ^{[1
]}

Dong, Ruipeng ^{[1
]}

机构：

[1] Univ Sci & Technol China, Sch Management, Int Inst Finance, Hefei 230026, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2021年 / 173卷

基金：

中国国家自然科学基金;

关键词：

Gaussian graphical models; Scalability; Confidence intervals; CONFIDENCE-INTERVALS; MATRIX ESTIMATION; LASSO; REGRESSION;

D O I：

10.1016/j.spl.2021.109085

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：

Statistical inference for ultra-large graphical models is important in network data analysis. We exploit the innovated scalable efficient estimation (Fan and Lv, 2016) as an initial estimate to develop a scalable inference procedure for graphical models. The effectiveness of the proposed method is theoretically and numerically demonstrated. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：6

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