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Disease mapping is an area of epidemiology concerned with estimating the spatial pattern in disease risk over a geographical region. Typically, the study area is partitioned into spatial districts (small areas), with the disease map displaying the disease incidence associated with each district. We consider Bayesian nonparametric small-area models with a Dirichlet process and a simultaneous conditional autoregressive model for the spatial correlation between neighboring areas to estimate the standardized mortality ratio and mapping. The observed mortality rates are unstable for regions with rare events such as cancer. Bayesian methods, similar to the proposed small-area analysis method, provide mechanisms for smoothing the estimates of rates by region. We compare nonparametric Bayesian models using a Dirichlet process prior with the parametric Bayesian models using a gamma prior for small-area estimation. [ABSTRACT FROM AUTHOR]

In a wireless sensor network, data collection is relatively cheap whereas data transmission is relatively expensive. Thus, preserving battery life is critical. If the process of interest is sufficiently predictable, the suppression in transmission can be adopted to improve efficiency of sensor networks because the loss of information is not great. The prime interest lies in finding an inference-efficient way to support suppressed data collection application. In this paper, we present a suppression scheme for a multiple nodes setting with spatio-temporal processes, especially when process knowledge is insufficient. We also explore the impact of suppression schemes on the inference of the regional processes under various suppression levels. Finally, we formalize the hierarchical Bayesian model for these schemes. [ABSTRACT FROM AUTHOR]