Abstract:
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Storm cells are the smallest component of a storm-producing system. A cluster of such cells is referred to as a storm and a storm system consists of a cluster of storms. This research develops a model for these storm cells over space and time. Specifically, we extend the Neyman-Scott process, which is commonly employed for the analysis of clustered point processes, to account for the hierarchical clustering present in our data. We do this by allowing the parents to follow a doubly stochastic process, namely a log-Gaussian Cox process. This model is applied to storm cell data from the Bismarck radar station in North Dakota, USA and parameter estimation is done using minimum contrast estimation.
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