When: Friday, November 14, 3:00 PM
Where: Tyler 055

Abstract
The motivation of this research is to improve inferences on the covariation in environmental exposures, motivated by data from a study of Toddlers Exposure to SVOCs in Indoor Environments (TESIE). The challenge is that the sample size is limited, so empirical covariance provides a poor estimate. In related applications, Bayesian factor models have been popular; these approaches express the covariance as low rank plus diagonal and can infer the number of factors adaptively. However, they have the disadvantage of shrinking towards a diagonal covariance, often under estimating important covariation patterns in the data. Alternatively, the dimensionality problem is addressed by collapsing the detailed exposure data within chemical classes, potentially obscuring important information. We apply a covariance meta regression extension of Bayesian factor analysis, which improves performance by including information from features summarizing properties of the different exposures. This approach enables shrinkage to more flexible covariance structures, reducing the over-shrinkage problem, as we illustrate in the TESIE data using various chemical features as meta covariates.

Bio
Elizabeth Bersson is a postdoctoral fellow with Tamara Broderick in the Laboratory for Information and Decision Systems at MIT. Previously, she received her PhD in statistical science from Duke University. Her research focuses on developing Bayesian methodology for correlated multivariate data.