Statistics and Computing

, Volume 24, Issue 1, pp 91–100

Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers

Authors

    • Department of Applied Mathematics and Informatics, Faculty of EconomicsUniversity of South Bohemia
  • M. Muška
    • Biology center of the AS CRInstitute of Hydrobiology
    • Faculty of ScienceUniversity of South Bohemia
  • J. Kubečka
    • Biology center of the AS CRInstitute of Hydrobiology
    • Faculty of ScienceUniversity of South Bohemia
Article

DOI: 10.1007/s11222-012-9355-3

Cite this article as:
Mrkvička, T., Muška, M. & Kubečka, J. Stat Comput (2014) 24: 91. doi:10.1007/s11222-012-9355-3

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.

Keywords

Bayesian methodComposite likelihoodClusteringInhomogeneous cluster centersInhomogeneous point processMinimum contrast methodModified K functionNeyman-Scott point process

Copyright information

© Springer Science+Business Media New York 2012