Jyotishka Datta, University of Arkansas
New directions in Bayesian sparse signal recovery Sparse signal recovery remains an important challenge in large scale data analysis and global-local (G-L) shrinkage priors have emerged as the current state-of-the art Bayesian method handling sparsity. In the first half of this talk, I will survey some of the recent theoretical and methodological advances in this area, focusing on theoretical optimality of G-L priors in the context of multiple testing for both continuous as well as quasi-sparse count data. In the second half, I will discuss a few unexplored aspects of their behavior, such as, validity as a non-convex regularization method, performance in presence of correlated errors or extension to discrete data structures including sparse compositional data. I will offer some insights into some of these problems and point out future directions.
