Joint Collaborative Statistics Colloquium: Jun Yan, University of Connecticut

Joint Collaborative Statistics Colloquium

Network analytics with applications to input-output tables

Presented by Professor Jun Yan, Department of Statistics, University of Connecticut

Thursday, Dec. 7 2023
8:00 PM-9:00 PM ET
(Friday, Dec. 8, 9 AM Beijing)
AUST 110
Webex Meeting Link
Voov Meeting Link (meeting id: 861-447-545)

Weighted, directed networks defined by national or multi-regional input-output tables are fundamental in dissecting the intricate economic structures and dynamics. Many global network metrics such as assortativity and centrality have been available for such analysis. Traditional network metrics often neglect auxiliary node-level features that hold key insights into these economic networks. Here I review several refined assortativity and centrality measures capable of integrating auxiliary information, thus enhancing the analysis of these networks tailored to the research objectives. The works include adapting assortativity measures for weighted rank associations and modifying PageRank and betweenness centrality to incorporate node-specific information. These metrics unlock new perspectives in the understanding of economic structures, potentially uncovering subtle yet crucial interdependencies both within and between economies. The practical application and efficacy of these methods are exemplified through simulations and a detailed comparative analysis of the economic networks of China and Japan.

Jun Yan is a Professor in the Department of Statistics at the University of Connecticut and a Research Fellow at the Center for Population Health at UConn Health. He received his PhD in statistics from the University of Wisconsin-Madison in 2003. Before joining UConn in 2007, he was on the faculty of the University of Iowa for four years. Dr. Yan's methodological research interests include network analysis, spatial extremes, measurement error, survival analysis, clustered data analysis, and statistical computing, most of which are motivated by his cross-disciplinary collaborations. His application domains are environmental sciences, public health, and sports. With a special interest in making his statistical methods available via open-source software, he and his coauthors developed and actively maintain a collection of R packages in the public domain. In 2020, he started editorship of the Journal of Data Science. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics.