Title: Spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution
Authors: Jonathan Bradley - Florida State University (United States) [presenting]
Abstract: A Bayesian approach is introduced for analyzing high-dimensional multinomial data that are recorded over space and time. In particular, the proportions associated with multinomial data are assumed to have a logit link to a latent spatio-temporal mixed effects model. This strategy allows for nonstationarity covariances in both space and time, asymmetry covariances, and dimension reduction. We also use the conditional multivariate logit-beta distribution, which leads to conjugate full-conditional distributions for use in a collapsed Gibbs sampler. Additionally, we provide methodological developments including (but not limited to): the derivation of the associated full-conditional distributions, a relationship with a latent Gaussian process model, and the stability of the non-stationary vector autoregressive model. We illustrate our model through simulations and through a demonstration with public-use quarterly workforce indicators data from the longitudinal employer household dynamics program of the US Census Bureau.