{"id":5694,"date":"2026-04-23T17:30:26","date_gmt":"2026-04-23T09:30:26","guid":{"rendered":"https:\/\/changjinyuan.com\/?p=5694"},"modified":"2026-05-01T15:03:53","modified_gmt":"2026-05-01T07:03:53","slug":"prof-jinyuan-changs-paper-accepted-by-journal-of-the-royal-statistical-society-series-b-2","status":"publish","type":"post","link":"https:\/\/changjinyuan.com\/index.php\/en\/latest-news-en\/5694\/","title":{"rendered":"Chang\u2019s Paper Accepted by Journal of the Royal Statistical Society Series B"},"content":{"rendered":"

The paper \u201cAutoregressive Networks with Dependent Edges\u201d, co-authored by Prof. Jinyuan Chang of the team, Dr. Qin Fang of the University of Sydney, Australia, Prof. Eric D. Kolaczyk of McGill University, Canada, Dr. Peter W. MacDonald of the University of Waterloo, and Prof. Qiwei Yao of the London School of Economics and Political Science, UK, has been officially accepted by the Journal of the Royal Statistical Society: Series B<\/em>, one of the leading international journals in the field of statistics.<\/p>\n

Abstract<\/strong><\/span><\/p>\n

We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity, and other stylized features often observed in real network data. By assuming the edges of networks at each time are independent conditionally on their lagged values, the models, which exhibit a close connection with temporal exponential random graph models, facilitate both simulation and the maximum likelihood estimation (MLE) in a straightforward manner. Due to the possibly large number of parameters in the models, the natural MLEs may suffer from slow convergence rates. An improved estimator for each component parameter is proposed based on an iteration employing projection, which mitigates the impact of the other parameters. Leveraging a martingale difference structure, the asymptotic distribution of the improved estimator is derived without the assumption of stationarity. The limiting distribution is not normal in general, although it reduces to normal when the underlying process satisfies some mixing conditions. Illustration with a transitivity model was carried out in both simulation and a real network data set.<\/p>\n

Author Introduction<\/strong><\/span><\/p>\n

Jinyuan Chang is the Executive Director of the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics. He is a Guanghua Chair Professor and a recipient of the National Science Fund for Distinguished Young Scholars of China. He primarily engaged in research related to complex data analysis.<\/p>\n

Qin Fang is an Assistant Professor at the University of Sydney. Her research interests include dynamic network analysis, functional data and time series analysis, and high-dimensional statistics.<\/p>\n

Eric D. Kolaczyk is a Professor at McGill University. His research interests include statistical and machine learning methods for primarily network data, typically supporting human endeavors enabled by computing and engineered systems.<\/p>\n

Peter W. MacDonald is an Assistant Professor at the University of Waterloo. He works primarily on statistical analysis and methods for multiple and dynamic networks. He also has interests in post-selection inference, and formally private and fair statistical methods for network data.<\/p>\n

Qiwei Yao is a Chair Professor at the London School of Economics and Political Science. His research interests include time series analysis, dimensionality reduction, factor modeling, dynamic network modeling, spatiotemporal modeling, financial econometrics, and nonparametric regression.<\/p>\n","protected":false},"excerpt":{"rendered":"

The paper \u201cAutoregressive Networks with Dependent Edges\u201d, co-authored by Prof. Jinyuan Chang of the team, Dr. Qin Fang of the University of Sydney, Australia, Prof. Eric D. Kolaczyk of McGill University, Canada, Dr. Peter W. MacDonald of the University of Waterloo, and Prof. Qiwei Yao of the London School of Economics and Political Science, UK, has been officially accepted by the Journal of the Royal Statistical Society: Series B, one of the leading international journals in the field of statistics.<\/p>\n","protected":false},"author":1,"featured_media":5553,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-5694","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-latest-news-en"],"acf":[],"lang":"en","translations":{"en":5694,"cn":5552},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/5694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/comments?post=5694"}],"version-history":[{"count":2,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/5694\/revisions"}],"predecessor-version":[{"id":5707,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/posts\/5694\/revisions\/5707"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media\/5553"}],"wp:attachment":[{"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/media?parent=5694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/categories?post=5694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/changjinyuan.com\/index.php\/wp-json\/wp\/v2\/tags?post=5694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}