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Defense: Spherical Latent Factor Model for Binary and Ordinal Data

Speaker Name: 
Xingchen Yu
Speaker Title: 
PhD Candidate
Speaker Organization: 
Statistical Science PhD
Start Time: 
Tuesday, September 22, 2020 - 2:00pm
End Time: 
Tuesday, September 22, 2020 - 3:00pm
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Abstract: Factor models are widely used across diverse areas of application for purposes that include dimensionality reduction, covariance estimation, and feature engineering. Traditional factor models can be seen as an instance of linear embedding methods that project multivariate observations onto a lower dimensional Euclidean latent space. This thesis discusses a new class of geometric embedding models for multivariate binary and ordinal data in which the embedding space correspond to a spherical manifold, with potentially unknown dimension. The resulting models include traditional factor models as a special case, but provide additional flexibility. Furthermore, unlike other techniques for geometric embedding, the models are easy to interpret, and the uncertainty associated with the latent features can be properly quantified. These advantages are illustrated using both simulation studies and real data on voting records from the U.S. Congress as well as survey applications.

Event Type: 
Abel Rodriguez
Graduate Program: 
Statistical Science PhD