Finite-sample equivalence in statistical models for presence-only data

DOI10.1214/13-AOAS667zbMath1283.62225arXiv1207.6950WikidataQ30876908 ScholiaQ30876908MaRDI QIDQ2441833

William Fithian, Trevor Hastie

Publication date: 28 March 2014

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.6950

zbMATH Keywords

maximum entropylogistic regressioncase-control samplingPoisson process modelsspecies modeling

Mathematics Subject Classification ID

Related Items (9)

Penalized composite likelihoods for inhomogeneous Gibbs point process models ⋮ Simulations of local Moran’s index in a spatio-temporal setting ⋮ Variable selection using penalised likelihoods for point patterns on a linear network ⋮ Analysis of presence-only data via exact Bayes, with model and effects identification ⋮ A general theory for preferential sampling in environmental networks ⋮ Multi-species distribution modeling using penalized mixture of regressions ⋮ Estimating animal utilization distributions from multiple data types: a joint spatiotemporal point process framework ⋮ Efficient modelling of presence-only species data via local background sampling ⋮ A conversation with Jerry Friedman

Uses Software

• ElemStatLearn

Cites Work

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• Modeling and spatial statistics
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• Presence‐Only Data and the EM Algorithm
• Practical Maximum Pseudolikelihood for Spatial Point Patterns
• Learning Theory
• Approximating Point Process Likelihoods with GLIM
• Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors
• Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology
• Fitting binary regression models with case-augmented samples

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