Date of Graduation


Document Type


Degree Name

Master of Science in Statistics and Analytics (MS)

Degree Level



Statistics and Analytics


John R. Tipton

Committee Member

Mark Arnold

Second Committee Member

Giovanni Petris


Autoregressive Model Parameter, Bayesian, Change Point Model, Parallel Computing, Prediction, Real-time Estimation, Seismic, Uncertainty


Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we conducted a simulation study where we could evaluate the predictive performance of the model framework. The results show that our method has acceptable performance of arrival time prediction with accounting for the uncertainty.