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X-WR-CALNAME;VALUE=TEXT:Latent Factor Regressions for the Social Sciences- Presenter: Brandon Stewart
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SUMMARY:Latent Factor Regressions for the Social Sciences- Presenter: Brandon Stewart
DESCRIPTION:<p><span><strong>Presenter: </strong>Brandon Stewart</span></p><p><span><strong>Abstract: </strong>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.</span></p>
LOCATION:1737 Cambridge Street, K354
STATUS:CONFIRMED
DTSTART:20140924T160000Z
DTEND:20140924T173000Z
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