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  • 统计与管理学院2018年学术报告第25期

    【主 题】 Aggregating Partial and Top Ranked Lists with Application to Genomic Studies

    【报告人】 Xinlei Wang  教授

    Southern Methodist University

    【时 间】 2018年06月25日(星期一)15:00-16:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】Rank aggregation has a rich history in the field of information retrieval, with applications to text mining, webpage ranking, meta-search engine building, etc. However, methods developed in such contexts are often ill suited for genomic applications, in which gene lists generated from individual biomedical studies are inherently noisy, due to various sources of heterogeneity. Further, because of missing or zero-count data, a portion of genes are not analyzed in all component studies, leading to partially ranked lists; and for some lists, only top-ranked genes are reported.   In this project, we conduct a comparative study to examine the performance characteristics of a collection of existing RA methods in genomic applications. Based on what was learned from numerical studies, we further develop a rigorous Bayesian latent variable approach to rank aggregation that formally deals with top and partial preference lists.

    嘉宾简介】Dr. Wang received her B.S. degree in Automatic Control from University of Science and Technology of China in 1997, and received her PhD degree in Decision Science and Statistics from University of Texas at Austin in 2002.  Currently, she is a Professor of Statistics at Southern Methodist University, USA. She conducts methodological research in multiple areas of Statistics, including Bayesian methods and applications, integrative analysis, meta-analysis, and order-related design, theory and inference. Dr. Wang has published 40+ methodological papers in Statistics including those in top statistical journals such as Journal of the American Statistical Association (JASA), Biometrika, Biometrics, Bioinformatics, etc. In the past, she has been the dissertation advisor of 12 Ph.D. students and 2 Master's students, as well as serving as a committee member for 13 Ph.D. graduates. Her research has been supported by multiple NSF and NIH grants, where she is the PI or Co-PI.