Self-Exciting models are statistical models of count data where the
probability of an event occurring is influenced by the history of the process.
In particular, self-exciting spatio-temporal models allow for spatial
dependence as well as temporal self-excitation. For large spatial or temporal
regions, however, the model leads to an intractable likelihood. An increasingly
common method for dealing with large spatio-temporal models is by using Laplace
approximations (LA). This method is convenient as it can easily be applied and
is quickly implemented. However, as we will demonstrate in this manuscript,
when applied to self-exciting Poisson spatial-temporal models, Laplace
Approximations result in a significant bias in estimating some parameters. Due
to this bias, we propose using up to sixth-order corrections to the LA for
fitting these models. We will demonstrate how to do this in a Bayesian setting
for Self-Exciting Spatio-Temporal models. We will further show there is a
limited parameter space where the extended LA method still has bias. In these
uncommon instances we will demonstrate how a more computationally intensive
fully Bayesian approach using the Stan software program is possible in those
rare instances. The performance of the extended LA method is illustrated with
both simulation and real-world data
