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McKelvey School of Engineering

Brown School

Statistics and Data Science Seminar: Harnessing The Collective Wisdom: Fusion Learning Using Decision Sequences From Diverse Sources

Thursday, November 16, 2023 | 4:00 PM - 5:00 PM

Cupples II, L015

Co-sponsored by TRIADS

Abstract: Learning from the collective wisdom of crowds enhances the transparency of scientific findings by incorporating diverse perspectives into the decision-making process. Synthesizing such collective wisdom is related to the statistical notion of fusion learning from multiple data sources or studies. However, fusing inferences from diverse sources is challenging since cross-source heterogeneity and potential data sharing complicate statistical inference. Moreover, studies may rely on disparate designs, employ widely different modeling techniques for inferences, and prevailing data privacy norms may forbid sharing even summary statistics across the studies for an overall analysis. In this talk, I will discuss an Integrative Ranking and Thresholding (IRT) framework for fusion learning in large-scale multiple testing. IRT operates under the setting where from each study a triplet is available: the vector of binary accept-reject decisions on the tested hypotheses, the study-specific False Discovery Rate (FDR) level and the hypotheses tested by the study. Under this setting, IRT constructs an aggregated, nonparametric, and discriminatory measure of evidence against each null hypotheses, which facilitates ranking the hypotheses in the order of their likelihood of being rejected. IRT is extremely flexible and guarantees an overall FDR control under arbitrary dependence between the evidence measures as long as the studies control their respective FDR at the desired levels. Furthermore, IRT synthesizes inferences from diverse studies irrespective of the underlying multiple testing algorithms employed by them.

This is a joint work with Bowen Gang (Fudan University) and Jianliang He (Fudan University).

Paper: arxiv.org/pdf/2308.11026.pdf

Bio: Trambak is an Assistant Professor of Analytics, Information and Operations Management (AIO) academic area at the University of Kansas School of Business. He received his Ph.D. in Business Statistics from the Marshall School of Business at the University of Southern California (USC). Prior to that, he obtained a Master degree in Statistics from the Indian Statistical Institute and a Bachelor degree in Statistics from St. Xavier’s College, Kolkata, India. Trambak is broadly interested in the development of rigorous statistical methods for analyzing modern high dimensional data when issues such as unobserved heterogeneity and noise accumulation in these datasets impede inferences using standard methods. The following are his current research interests: Nonparametric Empirical Bayes methods, Estimation and Prediction in Multivariate Mixed Models and Large-Scale Multiple Testing.

Host: Debashis Mondal

There will be a reception afterwards at 5:00 in the same room.

Schools

Arts & Sciences

Website

https://artsci.wustl.edu/events/stati...

Event Contact

sds@wustl.edu

Speaker Information
Speaker: Trambak Banerjee, University of Kansas
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