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Data were analysed for spatial clustering using the Bernoulli model and the significance of clusters were tested using the Kulldorff scan statistic.
All of these were identified using the Bernoulli model.
Using the Bernoulli model, clusters with increased relative risk were identified.
Six clusters were identified using the Bernoulli model after applying selection criteria (e.g. size of cluster).
In addition, we explored spatial clusters of Ciudad Real municipalities at high risk by using the Bernoulli model [ 29].
Clusters were detected using all models, but only clusters found using the Bernoulli model satisfied the selection criteria for further epidemiological analysis.
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In SaTScan we used the Bernoulli model, a circular scan window and allowed a maximum cluster size of 25%% of the population at risk.
Statistical significance for identified clusters was based on Monte Carlo simulation using the Bernoulli probability model option in SaTScan, which is most appropriate for case control data, adjusting for township population density [ 25].
The results based on the proposed method are compared to those obtained using the Bernoulli load model using four natural wind records to perform a nonlinear dynamic analysis of a three-span transmission line section.
Results were consistent with SaTScan cluster detection using the Bernoulli probability model.
By using the Bernoulli processes, a new model is introduced to describe the randomly occurred packet dropouts in transmission of neighbors' information.
More suggestions(15)
using the bernoulli load
using the Tesco model
using the bernoulli equation
using the Gaussian model
using the Lieberman model
using the Cox model
using the bernoulli distribution
using the bernoulli inequality
using the bernoulli theory
using the bernoulli repartition
using the Obama model
using the MTzoa model
using the Aphlis model
using the bernoulli measurement
using the bernoulli family
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