A0307
Title: Conditional probability tensor decompositions for multivariate categorical response regression
Authors: Xin Zhang - Florida State University (United States) [presenting]
Abstract: In many modern regression applications, the response consists of multiple categorical random variables whose probability mass is a function of a common set of predictors. We consider a new method for modeling such a probability mass function in settings where the number of response variables, the number of categories per response, and the dimension of the predictor are large. We introduce a latent variable model which implies a low-rank tensor decomposition of the conditional probability tensor. We derive an efficient and scalable penalized expectation-maximization algorithm to fit this model and examine its statistical properties.
