Mathematical Geosciences

, Volume 43, Issue 2, pp 243–259 | Cite as

Robust Resampling Confidence Intervals for Empirical Variograms

Article

Abstract

The variogram function is an important measure of the spatial dependencies of a geostatistical or other spatial dataset. It plays a central role in kriging, designing spatial studies, and in understanding the spatial properties of geological and environmental phenomena. It is therefore important to understand the variability attached to estimates of the variogram. Existing methods for constructing confidence intervals around the empirical variogram either rely on strong assumptions, such as normality or known variogram function, or are based on resampling blocks and subject to edge effect biases. This paper proposes two new procedures for addressing these concerns: a quasi-block-bootstrap and a quasi-block-jackknife. The new methods are based on transforming the data to decorrelate it based on a fitted variogram model, resampling blocks from the decorrelated data, and then recorrelating. The coverage properties of the new confidence intervals are compared by simulation to a number of existing resampling-based intervals. The proposed quasi-block-jackknife confidence interval is found to have the best properties of all of the methods considered across a range of scenarios, including normally and lognormally distributed data and misspecification of the variogram function used to decorrelate the data.

Keywords

Spatial analysis Variograms Bootstrap Jackknife Block bootstrap Block jackknife 

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Copyright information

© International Association for Mathematical Geosciences 2010

Authors and Affiliations

  1. 1.Centre for Statistical and Survey MethodologyUniversity of WollongongWollongongAustralia
  2. 2.Centre for Health Services DevelopmentUniversity of WollongongWollongongAustralia

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