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

0424217 - BC 2014 RIV NL eng J - Journal Article
Mrkvička, T. - Muška, Milan - Kubečka, Jan
Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers.
Statistics and Computing. Roč. 24, č. 1 (2014), s. 91-100. ISSN 0960-3174. E-ISSN 1573-1375
R&D Projects: GA ČR(CZ) GA206/07/1392
Institutional support: RVO:60077344
Keywords : bayesian method * clustering * inhomogeneous point process
Subject RIV: EH - Ecology, Behaviour
Impact factor: 1.623, year: 2014

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.
Permanent Link: http://hdl.handle.net/11104/0230256