Iranpanah, N, Mohammadzadeh, M and Taylor, CC (2011) A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics. Computational Statistics and Data Analysis, 55 (1). 578 - 587 . ISSN 0167-9473
Available under licence : See the attached licence file.
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.
|Copyright, Publisher and Additional Information:||© 2011, Elsevier. This is an author produced version of a paper published in Computational Statistics and Data Analysis. Uploaded in accordance with the publisher's self-archiving policy.|
|Keywords:||moving block bootstrap, semi-parametric bootstrap, plug-in kriging, Monte Carlo simulation, coal-ash data, random-fields, likelihood, covariance, prediction, regression|
|Institution:||The University of Leeds|
|Academic Units:||The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)|
|Depositing User:||Symplectic Publications|
|Date Deposited:||01 Nov 2012 14:17|
|Last Modified:||07 Jun 2014 02:35|