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We develop a sufficient dimension reduction paradigm for inhomogeneous spatial point processes driven by a Gaussian random fields. Specifically, we introduce the notion of the kth-order Central Intensity Subspace. We show that a Central Subspace (Cook, 1998) can be defined as the combination of...
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Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge...
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