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A0746
Title: A Bayesian spatial-temporal model with latent MLG random effects with application to earthquake magnitudes Authors:  Guanyu Hu - University of Connecticut (United States) [presenting]
Abstract: A Bayesian spatial-temporal model is introduced for analyzing earthquake magnitudes. Specifically, we define a spatial-temporal Pareto regression model with latent multivariate log-gamma random vectors to analyze earthquake magnitudes. This represents a marked departure from the traditional spatial generalized linear regression model, which uses latent Gaussian random effects. The multivariate log-gamma distribution results in a full-conditional distribution that can be easily sampled from, which leads to a fast mixing Gibbs sampler. Thus, our proposed model is a computationally efficient approach for modeling Pareto spatial data. The empirical results suggest similar estimation properties between the latent Gaussian model and latent multivariate log-gamma model, but our proposed model has stronger predictive properties. Additionally, we analyze a small US earthquake data set as an illustration of the effectiveness of our approach.