標(biāo)題：Challenge in the analysis of large scale data
報(bào)告時(shí)間：2025年07月12日（星期六）13:30-14:30
報(bào)告地點(diǎn)：人民大街校區(qū)惟真樓523室
主講人： Zhezhen Jin
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容簡(jiǎn)介：
Analysis of large-scale data is challenging due to data storage and computational complexity. When analyzing large-scale data, subsampling methods and divide-and-conquer procedures are appealing, because they ease the computational burden while preserving the validity of inferences. In this talk, several challenges and issues will be reviewed, and a perturbation subsampling approach will be presented based on independent and identically distributed stochastic weights for analyzing large-scale data. The method can be justified based on optimizing convex objective functions by establishing the asymptotic consistency and normality of the resulting estimators. The method simultaneously provides consistent point and variance estimators. We demonstrate the finite-sample performance of the proposed method using simulation studies and a real-data analysis.
主講人簡(jiǎn)介：
Professor Zhezhen Jin is a professor in the Department of Biostatistics at the Mailman School of Public Health, Columbia University. He is a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS), and served as the President of the International Chinese Statistical Association (ICSA) in 2022. He has long been engaged in research on statistical and biostatistical methodologies and has served as an associate editor for several top-tier statistical journals, including the Journal of the American Statistical Association (JASA).
