Abstract
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.
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We would like to thank to three referees and Samuel Soubeyrand for their helpful comments and Samuel Soubeyrand for checking the program codes.
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The work was supported by the Grant Agency of Czech Republic, Projects Nos. P201/10/0472 and 206/07/1392. The access to the MetaCentrum computing facilities, provided under the programme “Projects of Large Infrastructure for Research, Development, and Innovations” LM2010005 funded by the Ministry of Education, Youth, and Sports of the Czech Republic, is acknowledged.
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Mrkvička, T., Muška, M. & Kubečka, J. Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers. Stat Comput 24, 91–100 (2014). https://doi.org/10.1007/s11222-012-9355-3
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DOI: https://doi.org/10.1007/s11222-012-9355-3