Date and Time: 04/18/2025, 14:00--15:00

Location: 25 Park Place, Room 1441

Distinguished Lecture: Statistical Analysis of Weak Signals

Speaker: Professor Peter Song, Department of Biostatistics, University of Michigan

Speaker's website: https://sph.umich.edu/faculty-profiles/song-peter.html

Title: Statistical Analysis of Weak Signals

Abstract: The statistical analysis of weak signals (SAWS) is a fundamental challenge in various practical domains, including questionnaire items, agrochemical residues in food, genetic variants in DNA, daily physical activity, and virus detection in wastewater. In regression analysis, identifying individual associations of weak signals is often difficult due to limited sample sizes. As a result, signals are frequently grouped into bundles to enhance detectability. Supervised homogeneity pursuit is a popular approach for forming such bundles to achieve stronger associations with outcomes of interest. Recently, we proposed a novel SAWS framework that leverages mixed-integer optimization to simultaneously perform bundle formation, association estimation, and inference.  A technical innovation pertains to the reformulation of a grouping/clustering analysis as an estimation problem. This talk will discuss both the theoretical foundations and numerical performance of this approach.

References:

Wang, W, Wu, S, Zhu, Z, Zhou, L and Song, PXK (2024).  Supervised homogeneity fusion: A combinatorial approach. Annals of Statistics 52(1), 285-310.

Banker, MM, Zhang, L and Song, PXK (2024). Regularized scalar-on-function regression analysis to assess functional association of critical physical activity window with biological age.  Annals of Applied Statistics 18(4): 2730-2752.

Zhang, L, Zhang, Y, Xi, C and Song, PXK* (2024). Optimally monitoring a network of sewage manholes in infectious disease surveillance.  Statistica Sinica 35 (3).  DOI: 10.5705/ss.202022.0413

Speaker's biography: Dr. Song is Professor of Biostatistics at the School of Public Health in the University of Michigan, Ann Arbor. He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996. He has published over 230 peer-reviewed papers and graduated 26 PhD students and trained 6 postdoc research fellows. Dr. Song’s current research interests include data integration, distributed inference, high-dimensional data analysis, longitudinal data analysis, mediation analysis, spatiotemporal modeling, and applications in medicine and public health. He collaborates extensively with researchers from nutritional sciences, environmental health sciences, chronic diseases, infectious disease, aging and nephrology. He is IMS Fellow, ASA Fellow and Elected Member of the International Statistical Institute. Dr. Song now serves as Editor of the Annals of Applied Statistics and Associate Editor of the Journal of American Statistical Association and the Journal of Multivariate Analysis.

Host: Yichuan Zhao (yichuan@gsu.edu)