Regularized Estimating Equations for Model Selection of Clustered Spatial Point Processes

DOI10.5705/ss.2013.208wzbMath1400.62117OpenAlexW2325682636MaRDI QIDQ3195195

Yongtao Guan, Jun Zhu, Andrew L. Thurman, Rao Fu

Publication date: 21 October 2015

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/763c6018195e024e5d1ac77d20a0c43c0e5fd9ce

zbMATH Keywords

spatial statisticsvariable selectionspatial interactionweighted estimating equationsspatial point patternsecological application

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Numerical methods for ill-posed problems for initial value and initial-boundary value problems involving PDEs (65M30)

Related Items (7)

Penalized composite likelihoods for inhomogeneous Gibbs point process models ⋮ Variable selection using penalised likelihoods for point patterns on a linear network ⋮ Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes ⋮ Information criteria for inhomogeneous spatial point processes ⋮ Convex and non-convex regularization methods for spatial point processes intensity estimation ⋮ Regularized estimation for highly multivariate log Gaussian Cox processes ⋮ Sparse spatially clustered coefficient model via adaptive regularization

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