Fast Covariance Estimation for Innovations Computed from a Spatial Gibbs Point Process

DOI10.1111/sjos.12017zbMath1283.62198OpenAlexW2130588666MaRDI QIDQ2868860

Jean-François Coeurjolly, Ege Rubak

Publication date: 19 December 2013

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://vbn.aau.dk/da/publications/fast-covariance-estimation-for-innovations-computed-from-a-spatial-gibbs-point-process(ae9aa366-0ad9-4a6e-b197-48cfd8336a04).html

zbMATH Keywords

confidence intervalsmaximum pseudo-likelihoodinnovation processesexponential family modelsGeorgiiNguyenZessin formula

Mathematics Subject Classification ID

Related Items (4)

Penalized composite likelihoods for inhomogeneous Gibbs point process models ⋮ Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes ⋮ Pairwise interaction function estimation of stationary Gibbs point processes using basis expansion ⋮ Adjusted composite likelihood ratio test for spatial Gibbs point processes

Uses Software

• R
• spatstat

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Armenian connection: reminiscences from a time of interactions
• The Papangelou process
• \(R\)-local Delaunay inhibition model
• Properties of residuals for spatial point processes
• Pseudolikelihood for exponential family models of spatial point processes
• Gibbs measures and phase transitions
• Random fields
• A primer on perfect simulation for spatial point processes
• Maximum pseudolikelihood estimator for exponential family models of marked Gibbs point processes
• Takacs-Fiksel Method for Stationary Marked Gibbs Point Processes
• Integral and Differential Characterizations of the GIBBS Process
• Ergodic theorems for spatial processes
• Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
• Practical Maximum Pseudolikelihood for Spatial Point Patterns
• Statistical Analysis and Modelling of Spatial Point Patterns
• Modelling Spatial Point Patterns in R
• Residual Analysis for Spatial Point Processes (with Discussion)
• Score, pseudo-score and residual diagnostics for spatial point process models

This page was built for publication: Fast Covariance Estimation for Innovations Computed from a Spatial Gibbs Point Process