STATISTICS COLLOQUIUM
Moulinath Banerjee
Department of Statistics
University of Michigan
COMMUNICATION-EFFICIENT INTEGRATIVE REGRESSION IN HIGH DIMENSIONS
Abstract
We consider the task of meta-analysis in high-dimensional settings in which the data sources we wish to integrate are similar, but non-identical. To borrow strength across such heterogeneous data sources,
we introduce a global parameter, based on robustness considerations, that remains sparse even in the presence of outlier data sources. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the
combined dataset. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset.
This is joint work with Subha Maity and Yuekai Sun.
DATE: Wednesday, November 20, 2019
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu