Distinguishing different types of inhomogeneity in Neyman-Scott point processes

DOI10.1007/S11009-013-9365-4zbMath1305.62337OpenAlexW2022889039MaRDI QIDQ479146

Tomáš Mrkvička

Publication date: 5 December 2014

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11009-013-9365-4

zbMATH Keywords

clusteringBayesian methodNeyman-Scott point processgrowing clustersinhomogeneous cluster centersinhomogeneous point processlocation dependent scalingsecond order intensity reweighted stationaritytype of inhomogeneity

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) Bayesian inference (62F15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (4)

On the Bayesian estimation for the stationary Neyman-Scott point processes. ⋮ Inference for cluster point processes with over- or under-dispersed cluster sizes ⋮ Hidden Second-order Stationary Spatial Point Processes ⋮ A Bayesian hierarchical point process model for epidermal nerve fiber patterns

Cites Work

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• An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
• Statistical Analysis and Modelling of Spatial Point Patterns

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