电竞菠菜外围app

科学研究
报告题目:

Frequentist Model Averaging for Undirected Gaussian Graphical Models

报告人:

张新雨 教授(中国科学院数学与系统科学研究院)

报告时间:

报告地点:

腾讯会议ID: 859 670 439

报告摘要:

Advances in information technologies have made network data increasingly frequent in a spectrum of big data applications, which is often explored by probabilistic graphical models. To precisely estimate the precision matrix, we propose an optimal model averaging estimator for Gaussian graphs (MAEGG). We prove that the proposed estimator is asymptotically optimal when candidate models are misspecified and achieves sample consistency when at least one correct model is included in the candidate set. Furthermore, numerical simulations and a real data analysis on yeast genetic data were conducted to illustrate that the proposed method is promising.