Penalized composite likelihoods for inhomogeneous Gibbs point process models

DOI10.1016/j.csda.2018.02.005zbMath1469.62050OpenAlexW2793073182MaRDI QIDQ1662861

Jeffrey Daniel, Gary J. Umphrey, Julie Horrocks

Publication date: 20 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.02.005

zbMATH Keywords

regularizationvariable selectioncomposite likelihoodinformation criteriaGibbs point processspecies distribution modelling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (5)

What is the effective sample size of a spatial point process? ⋮ Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes ⋮ Information criteria for inhomogeneous spatial point processes ⋮ Adaptive Lasso and Dantzig selector for spatial point processes intensity estimation ⋮ Pairwise interaction function estimation of stationary Gibbs point processes using basis expansion

Uses Software

• glmnet
• hierNet
• ElemStatLearn
• pyuvdata

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