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A kernel‐based method for nonparametric estimation of variograms

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  • Keming Yu
  • Jorge Mateu
  • Emilio Porcu

Abstract

Variogram estimation plays an important role in many areas of spatial statistics. Potential areas of application include biology, ecology, economics and meteorology. However, it is common that, for example under highly correlated patterns, traditional estimators can not reflect all the spatial features or dependencies. In this paper, we present an alternative distribution‐free estimator based on nearest‐neighbour estimation with a non‐constant smoothing field that is better able to adapt to spatially varying features of the data pattern. We present a simulation study to compare our new estimator to a nearest‐neighbour estimator built with a constant smoothing parameter and to the classical variogram estimator. We apply our method to analyze two ecological data sets.

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  • Keming Yu & Jorge Mateu & Emilio Porcu, 2007. "A kernel‐based method for nonparametric estimation of variograms," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(2), pages 173-197, May.
    • Handle: RePEc:bla:stanee:v:61:y:2007:i:2:p:173-197
    DOI: 10.1111/j.1467-9574.2007.00326.x
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    Cited by:

    1. Tata Subba Rao & Granville Tunnicliffe Wilson & Tata Subba Rao & Gyorgy Terdik, 2017. "On the Frequency Variogram and on Frequency Domain Methods for the Analysis of Spatio-Temporal Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 308-325, March.

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