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12月7日 Yao Qiwei：Autoregressive networks and stylized features
- 来源：
- 学校官网
- 收录时间：
- 2024-12-11 09:42:46
- 时间：
- 2024-12-17 14:00:00
- 地点：
- 普陀校区理科大楼A1114
- 报告人：
- Yao Qiwei
- 学校：
- -/-
- 关键词：
- autoregressive networks, dynamic network processes, statistical inference, MLEs, permutation test, node heterogeneity, edge sparsity, persistence, transitivity, density dependence
- 简介：
- We give a brief introduction on the autoregressive (AR) model for dynamic network processes. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as MLEs and a permutation test for model diagnostic checking. We illustrate how this AR model can serve as a building block to accommodate more complex structures such as stochastic latent blocks, change-points. We also elucidate how some stylized features often observed in real network data, including node heterogeneity, edge sparsity, persistence, transitivity and density dependence, can be embedded in the AR framework. Then the framework needs to be extended for dynamic networks with dependent edges, which poses new technical challenges. Illustration with real network data for the practical relevance of the proposed AR framework is also presented.
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报告介绍：
We give a brief introduction on the autoregressive (AR) model for dynamic network processes. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as MLEs and a permutation test for model diagnostic checking. We illustrate how this AR model can serve as a building block to accommodate more complex structures such as stochastic latent blocks, change-points. We also elucidate how some stylized features often observed in real network data, including node heterogeneity, edge sparsity, persistence, transitivity and density dependence, can be embedded in the AR framework. Then the framework needs to be extended for dynamic networks with dependent edges, which poses new technical challenges. Illustration with real network data for the practical relevance of the proposed AR framework is also presented.
报告人介绍：
Yao Qiwei is a professor of statistics at the London School of Economics and Political Science, a Fellow of the Royal Statistical Society, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. He is an internationally renowned statistician who has been engaged in teaching and research in statistics. His main research areas include time series analysis, spatiotemporal process analysis, and financial econometrics. He has published over 80 academic papers and has received multiple research grants from UK national foundations such as EPSRC and BBSRC. His co-authored books include 'Nonlinear Time Series: Nonparametric and Parametric Methods' (with Jianqing Fan) published by Springer in 2003 and 'A Concise Course on Financial Econometrics' (with Jianqing Fan) published by Cambridge University Press in 2017. He currently serves as a joint editor of the Journal of the Royal Statistical Society and has previously served as an associate editor for top journals such as Annals of Statistics and Journal of the American Statistical Association, as well as a joint editor for Statistica Sinica.
