The paper “Bayesian Penalized Empirical Likelihood and MCMC Sampling”, co-authored by Prof. Jinyuan Chang, Prof. Chengyong Tang of Temple University (USA), and postdoctoral researcher Yuanzheng Zhu, has been officially accepted by the Journal of the Royal Statistical Society: Series B, one of the most prestigious journals in the field of statistics.

The paper “Statistical Inference for High-Dimensional Spectral Density Matrix”, co-authored by Prof. Jinyuan Chang, Qing Jiang of Beijing Normal University at Zhuhai, Senior Statistical Analyst Tucker McElroy from the U.S. Census Bureau, and Prof. Xiaofeng Shao from Washington University in St. Louis, has been officially accepted by the Journal of the American Statistical Association, a leading journal in the field of statistics.

The paper “On the Modeling and Prediction of High-Dimensional Functional Time Series”, co-authored by Prof. Jinyuan Chang, Assistant Prof. Qin Fang from the University of Sydney, Prof. Xinghao Qiao from the University of Hong Kong, and Prof. Qiwei Yao from the London School of Economics and Political Science, has been officially accepted by the Journal of the American Statistical Association, one of the top journals in the field of statistics.

The National Natural Science Foundation of China (NSFC) has recently announced the evaluation results of its Major Programs for 2024. The Major Program titled “Statistical Management Theory of Large-Scale Business Scenarios”, led by Prof. Jinyuan Chang from the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics (SWUFE), has been officially approved. The project is a joint effort with Prof. Weidong Liu of Shanghai Jiao Tong University, Researcher Xinyu Zhang from the Academy of Mathematics and Systems Science of the Chinese Academy of Sciences, Prof. Gang Kou of SWUFE, and Prof. Hansheng Wang of Peking University.

The National Natural Science Foundation of China recently announced the project evaluation results for 2024. The General Project titled “Ultra High-Dimensional (Conditional) Independence Tests and Their Applications in Economics”, led by Prof. Jing He, has been officially approved for funding.

The paper “Edge Differentially Private Estimation in the β-Model via Jittering and Method of Moments”, co-authored by Prof. Jinyuan Chang and his doctoral student Qiao Hu from the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics, Prof. Eric D. Kolaczyk from McGill University (Canada), Prof. Qiwei Yao from the London School of Economics and Political Science (UK), and Assistant Prof. Fengting Yi from Yunnan University, has been officially accepted by the Annals of Statistics, one of the world’s leading journals in the field of statistics.

Congratulations to our postdoctoral fellow Dr. Guanglin Huang, for his paper “Research on High-Dimensional Dynamic High-Order Moment Investment Portfolio Based on Factor Structure: Optimization and Semi-Parametric Estimation” (supervised by Prof. Wanbo Lu) has been selected for the 2023 Contemporary Economics PhD Innovation Project!

The paper “Statistical Inferences for Complex Dependence of Multimodal Imaging Data”, co-authored by Prof. Jinyuan Chang, Associate Prof. Jing He, and doctoral student Mingcong Wu from the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics (SWUFE), in collaboration with Prof. Jian Kang from the University of Michigan, has been officially accepted by Journal of the American Statistical Association, one of the most prestigious journals in the field of statistics.

The paper “Central Limit Theories for High-Dimensional Dependent Data”, co-authored by Prof. Jinyuan Chang from Southwestern University of Finance and Economics, Prof. Xiaohui Chen from the University of Illinois at Urbana-Champaign, and Ph.D. student Mingcong Wu from Southwestern University of Finance and Economics, has been officially accepted by Bernoulli, an internationally renowned academic journal in probability and statistics.