UCLA

Point processes have long been used as an effective modeling technique in the forecasting of earthquakes. In this dissertation, we evaluate the ability of earthquake focal mechanisms to predict the locational direction of future events and the usefulness of more complicated point process models including such covariates. We also introduce a new computational method for estimating parameters of point processes models using the Stoyan-Grabarnik estimator which avoids the need to numerically compute the intractable integral term needed to compute estimates via maximum likelihood.