Tech Talk: Dimension reduction for survival analysis with applications to cancer genomic data

  • August 4, 2022 from 12:00-1:00pm
  • Virtual presentation via BlueJeans (https://bluejeans.com/361095905) | Meeting ID 361095905
  • Please RSVP by emailing Debra Reese. Please include your full name, email address, and institution/organization.
  • This activity has been approved for AMA PRA Category 1 Credit. We will provide instructions on obtaining CME credit for attendance.

“Dimension reduction and robust nonparametric methods for high dimension survival data with applications to cancer genomic studies”

Sufficient dimension reduction (SDR) is known to be a powerful tool for achieving data reduction and visualization in statistical and machine learning problems. In this work, we develop a unified framework for high dimensional SDR problems (in the setting that the number of features is much larger than the sample size) with theoretical guarantees. In particular, we propose robust nonparametric SDR methods for high-dimensional survival analysis under weak modeling assumptions. This framework includes many popular survival models as special cases, and produces a number of practically useful outputs, including a uniformly consistent Kaplan-Meier-type estimator of the conditional survival function and conditional quantile function in high dimension. Promising applications are demonstrated through simulations and real data analysis on cancer genomic data.

Meet the Speaker

Shanshan Ding is an Associate Professor of Statistics in the Department of Applied Economics and Statistics at the University of Delaware. She is also a faculty council member at the Data Science Institute, and an affiliated faculty member with the Center for Bioinformatics and Computational Biology and the Center for Experimental and Applied Economics at UD. Prior to joining UD, she received her PhD degree in Statistics from the University of Minnesota-Twin Cities in 2014. Her research interests include dimension reduction, high dimensional and big data, statistical machine learning, statistical foundations of data science, application problems stemming from bioinformatics, neuroimaging, biomedical, social, and environmental sciences.

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