时 间：2023年3月16日 10:00-11:00
地 点：腾讯会议：750-473-781
报告人：赵秉鑫 助理教授 宾夕法尼亚大学
主持人：谌自奇 研究员 华东师范大学
摘 要：
Bingxin Zhao is an assistant professor in the Wharton Statistics and Data Science Department at the University of Pennsylvania. He received his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2020. Prior to joining UPenn, he was an assistant professor in the Department of Statistics at Purdue University from 2020 to 2022. He is interested in statistical methods & applications to big data, high-dimensional statistics, biomedical & biological data science, environmental data science, and social data science. He has published papers in Science, Nature Genetics, Natural Communications, Genetic Epidemiology, JASA, Biometrics, etc.
报告人简介：
Numerous statistical models have been proposed for genetic prediction using highdimensional data from genome-wide association studies. The relative performance of these methods varies with the dataset characteristics and underlying genetic architecture of the targeted traits/diseases. Motivated by empirical observations, we present a series of analyses on genetic prediction methods in a high-dimensional sparsity-free setting, where we are allowed to have few to many true signals. In particular, I will present our analyses of two statistical problems. In the first project, we quantify the asymptotic bias when estimating genetic relationships with other traits based on polygenic risk scores (Zhao, Yang, and Zhu, 2022, arXiv). The second project is to understand the impact of reference panels on genetic prediction accuracy (Zhao, Zheng, and Zhu, 2022, arXiv). These analyses are based on recent advances in random matrix theory.