时 间：2025年6月12 日（周四）13：00 – 14：00
地 点：普陀校区理科大楼A1514室 、 腾讯会议ID ：732-175-877
报告人：吴月华 加拿大约克大学教授
主持人：汤银才 华东师范大学教授
摘 要：
In this talk, we consider data integration problem where correlated data are collected from multiple platforms. Within each platform, there are linear relationships between the responses and a collection of predictors. We extend the linear models to include random errors coming from a much wider family of sub-Gaussian and sub-exponential distributions. The goal is to select important predictors across multiple platforms, where the number of predictors and the number of observations both increase to infinity. We combine the marginal densities of the responses obtained from different platforms to form a composite likelihood and propose a model selection criterion based on Bayesian composite posterior probabilities. Under some regularity conditions, we prove that the model selection criterion is consistent with divergent true model size. We implement a Monte Carlo Markov Chain algorithm to conduct the model selection approach. We further present simulation results and a real data example.
报告人简介：
吴月华，加拿大约克大学数学与统计系教授。师从世界著名统计学家 C. R. Rao，于1989年获得美国匹兹堡大学统计学博士学位。目前，她从事高维数据分析、模型选择、变点分析、时空建模、环境统计和统计金融等多领域研究，是国际统计学会的当选会员。她在PNAS, Biometrika, Journal of Economics等期刊上发表了145余篇学术论文，也一直承担加拿大国家自然科学基金科研项目。另外，她目前是Entropy 特刊“用于对高维和复杂数据进行建模的统计方法：第二期”的客座编辑，Springer Nature 系列丛书“数学奇迹：本着 CR Rao 精神的文本和专著”编辑委员会副主编，Statsitical Theory and Related Fields 副主编。