#  Latent Factor Regressions for the Social Sciences- Presenter: Brandon Stewart 

 



####  calendar\_today Date and Time 

 **September 24, 2014** 

 12:00PM - 01:30PM EDT 

####  pin\_drop Location 

 **1737 Cambridge Street, K354**  



 

 



 

**Presenter:** Brandon Stewart

**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.



 

 



 

 

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