Variable selection for spatial Poisson point processes via a regularization method

DOI10.1016/j.stamet.2013.08.001zbMath1486.62253OpenAlexW2004910542MaRDI QIDQ1731184

Jun Zhu, Andrew L. Thurman

Publication date: 13 March 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2013.08.001

zbMATH Keywords

maximum likelihood estimationmodel selectionspatial statisticsintensity functionadaptive Lasso

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Ridge regression; shrinkage estimators (Lasso) (62J07) General biostatistics (92B15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (6)

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 ⋮ 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 ⋮ Efficient modelling of presence-only species data via local background sampling

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