Statistics and Computing

, Volume 24, Issue 1, pp 91–100 | Cite as

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

Article

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 method Composite likelihood Clustering Inhomogeneous cluster centers Inhomogeneous point process Minimum contrast method Modified K function Neyman-Scott point process 

Notes

Acknowledgements

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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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Applied Mathematics and Informatics, Faculty of EconomicsUniversity of South BohemiaČeské BudějoviceCzech Republic
  2. 2.Biology center of the AS CRInstitute of HydrobiologyČeské BudějoviceCzech Republic
  3. 3.Faculty of ScienceUniversity of South BohemiaČeské BudějoviceCzech Republic

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