Variable selection for inhomogeneous spatial point process models

DOI10.1002/cjs.11244zbMath1328.62552OpenAlexW1974029608MaRDI QIDQ5256382

Ji Meng Loh, Yu Ryan Yue

Publication date: 22 June 2015

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

Full work available at URL: https://doi.org/10.1002/cjs.11244

zbMATH Keywords

intensity functionspatial point processesmaximum pseudo-likelihood estimatorBerman-Turner approximationvariable selection via regularizationweighted Poisson likelihood

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Penalized composite likelihoods for inhomogeneous Gibbs point process models ⋮ What is the effective sample size of a spatial point process? ⋮ Variable selection using penalised likelihoods for point patterns on a linear network ⋮ Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes ⋮ Adaptive Lasso and Dantzig selector for spatial point processes intensity estimation ⋮ Data-driven chimney fire risk prediction using machine learning and point process tools ⋮ Convex and non-convex regularization methods for spatial point processes intensity estimation ⋮ Analyzing order flows in limit order books with ratios of Cox-type intensities

Uses Software

• R
• spatstat
• spatial
• glmnet

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Consistent parametric estimation of the intensity of a spatial-temporal point process
• Pathwise coordinate optimization
• Two-Step Estimation for Inhomogeneous Spatial Point Processes
• Bayesian Semiparametric Intensity Estimation for Inhomogeneous Spatial Point Processes
• Adapting to Unknown Smoothness via Wavelet Shrinkage
• Estimating functions for inhomogeneous spatial point processes with incomplete covariate data
• Estimator for the pair–potential of a gibbsian point process
• A model for clustering
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Practical Maximum Pseudolikelihood for Spatial Point Patterns
• Approximating Point Process Likelihoods with GLIM
• Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology
• Statistical Inference for Spatial Processes
• Bayesian nonparametric estimation of pair correlation function for inhomogeneous spatial point processes
• Regularization and Variable Selection Via the Elastic Net
• An Estimating Function Approach to Inference for Inhomogeneous Neyman–Scott Processes
• Model Selection and Estimation in Regression with Grouped Variables
• Statistical Analysis and Modelling of Spatial Point Patterns

This page was built for publication: Variable selection for inhomogeneous spatial point process models