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Bayesian Spatial Survival Models for Political Event Processes ∗

By David Darmofal, Lyndsey Young Stanfill and Tracy Sulkin


Research in political science is increasingly, but independently, modeling heterogeneity and spatial dependence in political processes. This paper draws together these two research agendas via spatial random effects survival models. In contrast to existing survival models in political science, which assume spatial independence, spatial survival models allow for spatial autocorrelation in random effects at neighboring locations, which will occur if we are unable to model fully the sources of spatial autocorrelation in our data. I examine spatially dependent random effects in both semiparametric Cox and parametric Weibull models, and examine these random effects in both individual and hierarchical frailty models. I employ a Bayesian approach in which spatial autocorrelation in unmeasured risk factors across neighboring units is incorporated into the model via a conditionally autoregressive (CAR) prior. I apply the Bayesian spatial survival modeling approach to the timing of U.S. House members ’ position announcements on the North American Free Trade Agreement (NAFTA). I find that spatial shared frailty models outperform standard non-frailty models and non-spatial frailty models in both the semiparametric and parametric analyses. The modeling of the spatial dependence in the random effects also produces changes in the effects of substantive covariates

Year: 2013
OAI identifier: oai:CiteSeerX.psu:
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