A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics

Authors:
N. Iranpanah;M. Mohammadzadeh;C. C. Taylor
Affiliations:
Department of Statistics, University of Isfahan, 8174673441, Isfahan, Iran;Department of Statistics, Tarbiat Modares University, P.O. Box 14115-175 Tehran, Iran;Department of Statistics, University of Leeds, Leeds LS2 9JT, UK
Venue:
Computational Statistics & Data Analysis
Year:
2011

Citing 2
Cited 1

Nonparametric resampling for homogeneous strong mixing random fields

Journal of Multivariate Analysis
Spectral approximation to the likelihood for an intrinsic Gaussian random field

Journal of Multivariate Analysis

Ensemble classification of paired data

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data.