Innovated scalable dynamic learning for time-varying graphical models

被引：0

作者：

Zheng, Zemin ^{[1
]}

Li, Liwan ^{[1
]}

Zhou, Jia ^{[1
]}

Kong, Yinfei ^{[2
]}

机构：

[1] Univ Sci & Technol China, Sch Management, Hefei, Anhui, Peoples R China

[2] Calif State Univ Fullerton, Dept Informat Syst & Decis Sci, Fullerton, CA 92634 USA

来源：

STATISTICS & PROBABILITY LETTERS | 2020年 / 165卷

基金：

中国国家自然科学基金;

关键词：

Time-varying graphical models; Precision matrix estimation; Scalability; Kernel smoothing; PRECISION MATRIX ESTIMATION; SPARSE; SELECTION; LASSO;

D O I：

10.1016/j.spl.2020.108843

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 paper, we propose a new approach of innovated scalable dynamic learning (ISDL) for estimating time-varying graphical structures. Motivated by the innovated transformation, we convert the original problem into large covariance matrix estimation and exploit the scaled Lasso with kernel smoothing to simplify the tuning procedure. In addition, we show that our method has theoretical guarantees under mild regularity conditions for accurate estimation of each precision matrix. (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：6

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