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

DOI10.1007/s13253-019-00380-4OpenAlexW2984010609WikidataQ126853080 ScholiaQ126853080MaRDI QIDQ782727

Jeffrey Daniel, Julie Horrocks, Gary J. Umphrey

Publication date: 28 July 2020

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-019-00380-4

zbMATH Keywords

logistic regressionspatial point processescase-control samplingspecies distribution modelling

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Uses Software

• blockCV
• Maxent

Cites Work

• Local case-control sampling: efficient subsampling in imbalanced data sets
• Poisson point process models solve the ``pseudo-absence problem for presence-only data in ecology
• Variable selection for spatial Poisson point processes via a regularization method
• Spatial logistic regression and change-of-support in Poisson point processes
• Finite-sample equivalence in statistical models for presence-only data
• A Kernel Method for Smoothing Point Process Data
• 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

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