Speaker Profile

CHANG Jinyuan is a Professor and Doctoral Supervisor at School of Statistics and Data Science, Southwestern University of Finance and Economics, and Executive Director of Joint Laboratory for Data Science and Business Intelligence. He is a recipient of a national-level talent award, with research focusing on the analysis of large-scale, complex data. He is an Associate Editor for top-tier international academic journals, including Journal of the Royal Statistical Society Series B, Journal of Business & Economic Statistics, and Journal of the American Statistical Association. He is the recipient of numerous prestigious awards, such as State Council Special Government Allowance, the First Prize of Young Scientist Award from Fok Ying Tung Education Foundation, Award for Outstanding Scientific Research in Higher Education from Ministry of Education, and Sichuan Youth Science and Technology Award.

Abstract

In the era of big data, developing effective methods for solving increasingly complex econometric models is of critical theoretical and practical importance. This presentation will introduce a comprehensive methodological framework, built upon empirical likelihood, for tackling ultra-high-dimensional econometric models.

The presentation will focus on two key areas:

1. Effective estimation and inference for model parameters.

2. Leveraging Bayesian sampling to address the prevalent non-convex optimization challenges inherent in econometric modeling.

Date/Time: October 22, 10:30 a.m.

Venue: Room 0204, 0 Teaching Building, Jiuliu Campus