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

DOI10.1016/j.csda.2021.107282OpenAlexW3165441604MaRDI QIDQ2242045

Ronny Vallejos, Jonathan Acosta, Felipe Osorio, Alfredo Alegria

Publication date: 9 November 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2021.107282

zbMATH Keywords

covariance functionFisher informationMatérn modelgodambe information

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• AS 136

Cites Work

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• Flexible and efficient estimating equations for variogram estimation
• Efficient maximum approximated likelihood inference for Tukey's \(g\)-and-\(h\) distribution
• Composite likelihood estimation for a Gaussian process under fixed domain asymptotics
• A case study competition among methods for analyzing large spatial data
• Likelihood approximation with hierarchical matrices for large spatial datasets
• On the effective geographic sample size
• The Effective Sample Size
• The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method

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