On optimal spatial subsample size for variance estimation

DOI10.1214/009053604000000779zbMath1056.62055arXivmath/0503671OpenAlexW2142093123MaRDI QIDQ1766124

Daniel J. Nordman, Soumendra Nath Lahiri

Publication date: 28 February 2005

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

Full work available at URL: https://arxiv.org/abs/math/0503671

zbMATH Keywords

spatial statisticsblock bootstrapvariance estimationblock sizelattice point countsubsampling method

Mathematics Subject Classification ID

Random fields (60G60) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Nonparametric statistical resampling methods (62G09)

Related Items (16)

Bias expansion of spatial statistics and approximation of differenced lattice point counts ⋮ Bootstrapping the empirical distribution function of a spatial process ⋮ Scalable semiparametric spatio-temporal regression for large data analysis ⋮ Computation of lacunarity from covariance of spatial binary maps ⋮ Resampling methods for spatial regression models under a class of stochastic designs ⋮ An empirical likelihood method for spatial regression ⋮ A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics ⋮ Self‐normalization for Spatial Data ⋮ Subsampling weakly dependent time series and application to extremes ⋮ On optimal spatial subsample size for variance estimation ⋮ Subsampling Variance Estimation for Non‐stationary Spatial Lattice Data ⋮ Uncertainty quantification in robust inference for irregularly spaced spatial data using block bootstrap ⋮ Frequency domain bootstrap methods for random fields ⋮ Bootstrapping an inhomogeneous point process ⋮ The Dependent Random Weighting ⋮ A frequency domain empirical likelihood method for irregularly spaced spatial data

Uses Software

• spatial
• AS 312

Cites Work

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