Jyotishka Datta - Colloquium Speaker

Assistant Professor, Department of Mathematical Sciences, University of Arkansas
Thursday, September 10, 2020 - 3:15pm
Colloquium Title: 
New Directions in Bayesian Shrinkage for Structured Data


Sparse signal recovery remains an important challenge in large scale data analysis and global-local (G-L) shrinkage priors have undergone an explosive development in the last decade in both theory and methodology. These developments have established the G-L priors as the state-of-the-art Bayesian tool for sparse signal recovery as well as default non-linear problems. While there is a huge literature proposing elaborate shrinkage and sparsity priors for high-dimensional real-valued parameters, there has been limited consideration of discrete data structures. In the first half of this talk, I will survey the recent advances in G-L shrinkage priors, focusing on the optimality of these priors for both continuous as well as quasi-sparse count data. In the second half, I will discuss an extension to discrete data structures including sparse compositional data, routinely occurring in microbiomics. I will discuss the methodological challenges with the Dirichlet distribution as a shrinkage prior to high-dimensional probabilities for its inability to adapt to an arbitrary level of sparsity and propose to address this gap by using a new prior distribution, specially designed to enable scaling to data with many categories. I will provide some theoretical support for the proposed methods and show improved performance in several simulation settings and applications to microbiome data.


Everyone is welcome to join! Please note that the meeting opens at 3:15pm, and the presentation is at 3:30-4:20pm. There will be time afterward for Q&A with the speaker.

Topic: Colloquia: Department of Statistics and Actuarial Science, University of Iowa

Time: Sep 10, 2020 03:15 PM Central Time (US and Canada)

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Meeting ID: 910 5155 4022