Date: 2019-03-01

Time: 15:30-16:30

Location: BURN 1104

Abstract:

The likelihood function is central to many statistical procedures, but poses challenges in classical and modern data settings. Motivated by cell lineage tracking experiments to study hematopoiesis (the process of blood cell production), we present recent methodology enabling likelihood-based inference for partially observed data arising from continuous-time branching processes. These computational advances allow principled procedures such as maximum likelihood estimation, posterior inference, and expectation-maximization (EM) algorithms in previously intractable data settings. We then discuss limitations and alternatives when data are very large or generated from a hidden process, and potential ways forward using ideas from sparse optimization.

Speaker

Jason Xu is an Assistant Professor of Statistical Science at Duke University. Prior to joining the department, he was supported by the NSF Mathematical Sciences Postdoctoral Research Fellowship at the University of California Los Angeles. He completed his PhD in Statistics at the University of Washington advised by Prof. Vladimir Minin.