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A kernel‐based method for nonparametric estimation of variograms
Author(s) -
Yu Keming,
Mateu Jorge,
Porcu Emilio
Publication year - 2007
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2007.00326.x
Subject(s) - estimator , variogram , smoothing , kernel smoother , nonparametric statistics , kernel (algebra) , field (mathematics) , constant (computer programming) , mathematics , computer science , statistics , kernel density estimation , kernel method , artificial intelligence , kriging , support vector machine , combinatorics , radial basis function kernel , pure mathematics , programming language
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.