Our speaker this Wednesday (9/24) at Applied Stats will be our own Brandon Stewart, who will be practicing his job talk.  Brandon will be giving a talk entitled Latent Factor Regressions for the Social Sciences. The abstract for the talk is included below. As per usual, we will meet in CGIS K354 at 12 noon and lunch will be served.

I look forward to seeing you all there! Thank you!

Abstract: I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiples types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although, related models have previously been proposed in several different fields, inference is typically cumbersome and slow. I introduce a class of fast variational inference algorithms that allow for models to be fit quickly and accurately.