Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers
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- Mrkvička, T., Muška, M. & Kubečka, J. Stat Comput (2014) 24: 91. doi:10.1007/s11222-012-9355-3
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This paper is concerned with parameter estimation for the Neyman-Scott point process with inhomogeneous cluster centers. Inhomogeneity depends on spatial covariates. The regression parameters are estimated at the first step using a Poisson likelihood score function. Three estimation procedures (minimum contrast method based on a modified K function, composite likelihood and Bayesian methods) are introduced for estimation of clustering parameters at the second step. The performance of the estimation methods are studied and compared via a simulation study. This work has been motivated and illustrated by ecological studies of fish spatial distribution in an inland reservoir.