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

DOI10.1007/S11222-012-9355-3zbMath1325.62077OpenAlexW2000148136MaRDI QIDQ892449

J. Kubečka, Tomáš Mrkvička, M. Muška

Publication date: 19 November 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-012-9355-3

zbMATH Keywords

clusteringBayesian methodcomposite likelihoodNeyman-Scott point processinhomogeneous cluster centersinhomogeneous point processminimum contrast methodmodified \(K\) function

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Markov processes: estimation; hidden Markov models (62M05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (5)

On the Bayesian estimation for the stationary Neyman-Scott point processes. ⋮ Inference for cluster point processes with over- or under-dispersed cluster sizes ⋮ Distinguishing different types of inhomogeneity in Neyman-Scott point processes ⋮ Estimating second-order characteristics of inhomogeneous spatio-temporal point processes ⋮ Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates

Cites Work

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• A Composite Likelihood Approach in Fitting Spatial Point Process Models
• Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
• An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
• Statistical Analysis and Modelling of Spatial Point Patterns

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