On the effective geographic sample size

DOI10.1080/00949655.2018.1428977OpenAlexW2790296567MaRDI QIDQ4960659

Jonathan Acosta, Daniel Griffth, Ronny Vallejos

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2018.1428977

zbMATH Keywords

covariance functionGaussian processhypothesis testingincreasing domainspatial regression model

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Editorial ⋮ Assessing the effective sample size for large spatial datasets: a block likelihood approach

Cites Work

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• Effective sample size for line transect sampling models with an application to marine macroalgae
• Maximum likelihood estimation for directional conditionally autoregressive models
• Effective sample size for spatial regression models
• Estimation of the mean for spatially dependent data belonging to a Riemannian manifold
• Accounting for spatial correlation in the scan statistic
• Maximum likelihood estimation of models for residual covariance in spatial regression
• The Effective Sample Size
• The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method
• Model-free estimation from spatial samples: a reappraisal of classical sampling theory

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