Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/89422
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Type: Journal article
Title: Robust and resistant semi-variogram modelling using a generalized bootstrap
Author: Olea, R.
Pardo-Iguzquiza, E.
Dowd, P.
Citation: Journal of the Southern African Institute of Mining and Metallurgy, 2015; 155(1):37-44
Publisher: Southern African Institute of Mining and Metallurgy
Issue Date: 2015
ISSN: 2225-6253
2411-9717
Statement of
Responsibility: 
R.A. Olea, E. Pardo-Igúzquiza, and P.A. Dowd
Abstract: The bootstrap is a computer-intensive resampling method for estimating the uncertainty of complex statistical models. We expand on an application of the bootstrap for inferring semivariogram parameters and their uncertainty. The model fitted to the median of the bootstrap distribution of the experimental semivariogram is proposed as an estimator of the semivariogram. The proposed application is not restricted to normal data and the estimator is resistant to outliers. Improvements are more significant for data-sets with less than 100 observations, which are those for which semivariogram model inference is the most difficult. The application is illustrated by using it to characterize a synthetic random field for which the true semivariogram type and parameters are known.
Keywords: geostatistics; sampling distribution; median; normal score transformation; ordinary least-squares fitting
Rights: © The Southern African Institute of Mining and Metallurgy, 2015.
DOI: 10.17159/2411-9717/2015/v115n1a4
Grant ID: http://purl.org/au-research/grants/arc/DP110104766
Published version: http://www.saimm.co.za/Journal/v115n01p037.pdf
Appears in Collections:Aurora harvest 7
Civil and Environmental Engineering publications

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