Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-10-31 DOI:10.1080/10618600.2024.2421246

Jingfei Zhang, Yi Li

引用次数: 0

Abstract

Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumbe...

查看原文本刊更多论文

高斯图形回归与高维变量的多任务学习

高斯图形回归是将高斯图形模型的精度矩阵与协变因素进行回归的一种强大方法，它允许响应变量和协变因素的数量大于响应变量和协变因素的数量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.