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Activity Number: 513 - ENVR Student Paper Awards
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #327111 Presentation
Title: A Bayesian Spatial-Temporal Model with Latent Multivariate Log-Gamma Random Processes with Application to Earthquake Magnitudes
Author(s): Guanyu Hu* and Jonathan R Bradley
Companies: University of Connecticut and Florida State University
Keywords: Pareto Regression; Multivariate Log-gamma Process; Earthquake Magnitudes

We introduce a Bayesian spatial-temporal model 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 processes. The multivariate log-gamma process 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 process model and latent multivariate log-gamma model, but our proposed model has stronger predictive properties. Additionally, we analyze a US earthquake dataset as an illustration of the effectiveness of our approach.

Authors who are presenting talks have a * after their name.

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