專題演講 主講人：黃瓊玉教授（Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, USA）
題 目：Synthesizing external aggregated information in the presence of population heterogeneity: a penalized empirical likelihood approach
主講人：黃瓊玉教授
（Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, USA）
時 間：110年3月5日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所428室舉行)
地 點：交大綜合一館427室
摘要
With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from external sources to improve the analysis of smaller-scale studies. An emerging challenge in the era of big data is that the subject-level data are high-dimensional, but the external information is at an aggregate level and of a lower dimension. Moreover, heterogeneity and uncertainty in the auxiliary information are often not accounted for in information synthesis. In this paper, we propose a unified framework to summarize various forms of aggregated information via estimating equations and develop a penalized empirical likelihood approach to incorporate such information in logistic regression. When the homogeneity assumption is violated, we extend the method to account for population heterogeneity among different sources of information. When the uncertainty in the external information is not negligible, we propose a variance estimator adjusting for the uncertainty. The proposed estimators are asymptotically more efficient than the conventional penalized maximum likelihood estimator and enjoy the oracle property even with a diverging number of predictors. Simulation studies show that the proposed approaches yield higher accuracy in variable selection compared with competitors. We illustrate the proposed methodologies with a pediatric kidney transplant study.
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