Logistic regression for spatial Gibbs point processes

DOI10.1093/biomet/ast060zbMath1452.62700OpenAlexW2079919087MaRDI QIDQ2874951

Jean-François Coeurjolly, Ege Rubak, Rasmus Waagepetersen, Adrian J. Baddeley

Publication date: 13 August 2014

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/536a79b398454591657bc33cffb75a8cf8cd0f3f

zbMATH Keywords

logistic regressionestimating functionpseudolikelihoodexponential family modelGeorgii-Nguyen-Zessin formula

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Generalized linear models (logistic models) (62J12) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (11)

Penalized composite likelihoods for inhomogeneous Gibbs point process models ⋮ Should We Condition on the Number of Points When Modelling Spatial Point Patterns? ⋮ Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes ⋮ The integrated nested Laplace approximation applied to spatial log-Gaussian Cox process models ⋮ Pairwise interaction function estimation of stationary Gibbs point processes using basis expansion ⋮ Data-driven chimney fire risk prediction using machine learning and point process tools ⋮ Spatiotemporal wildfire modeling through point processes with moderate and extreme marks ⋮ Convex and non-convex regularization methods for spatial point processes intensity estimation ⋮ The Poisson transform for unnormalised statistical models ⋮ Adjusted composite likelihood ratio test for spatial Gibbs point processes ⋮ Stochastic Quasi-Likelihood for Case-Control Point Pattern Data

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