Synthesizing External Aggregated Information in the Presence of Population Heterogeneity: A Penalized Empirical Likelihood Approach

December 2, 2020
3:00 - 4:00pm
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In the era of big data, it is challenging to exploit external auxiliary information to improve the analysis of smaller-scale studies because the subject-level data are high-dimensional while 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. Sheng will discuss a unified penalized empirical likelihood framework that tackles these problems in combining information from different sources.


Image previewSpeaker: Ying Sheng, PhD, Postdoctoral Scholar, Department of Epidemiology and Biostatistics, UCSF



Event Type: 
Biostatistics and Bioinformatics Seminar