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As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get(More)
We compare convergence rates of Metropolis–Hastings chains to multi-modal target distributions when the proposal distributions can be of " local " and " small world " type. In particular, we show that by adding occasional long-range jumps to a given local proposal distribution , one can turn a chain that is " slowly mixing " (in the complexity of the(More)
We study the problem of suitably locating US Coast Guard air stations to respond to emergency distress calls. Our goal is to identify robust locations in the presence of uncertainty in distress call locations. Our analysis differs from the literature primarily in the way we model this uncertainty. In our optimization and simulation based methodology, we(More)
This report describes a simulation study of the results in Waagepetersen and Guan (2008). We refer to this paper and Waagepetersen (2007) for background on the simulation study. 1. Simulation study To check how the asymptotic results in Waagepetersen and Guan (2008) apply in finite-sample settings we consider simulation studies for an inhomogeneous Thomas(More)
We propose a formal method to test stationarity for spatial point processes. The proposed test statistic is based on the integrated squared deviations of observed counts of events from their means estimated under stationarity. We show that the resulting test statistic converges in distribution to a functional of a two-dimensional Brownian motion. To conduct(More)
Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time-dependent covariates may lead to biased results, especially when(More)
In disease surveillance applications, the disease events are modeled by spatio-temporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatio-temporal process as spatially correlated(More)
In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary(More)