• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.327-336
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• 2004

Many researchers have used the robust variogram to reduce the effect of outliers in spatial data analysis. Recently it is known that estimating the variogram after replacing outliers is more efficient. In this paper, we suggest a new data cleaner for geostatistic data analysis and compare the efficiency of outlier cleaners.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.519-533
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• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.

• Journal of applied mathematics & informatics
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• v.4 no.2
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• pp.419-432
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• 1997

A robust estimator for interpolating spatially distributed regionalized variables is introduced. It reduces outlier effects on ob-taining correlation between spatial lags and the correlation between spatial lags and the corresponding semi-variances and produces a significaantly improved semivariogram com-pared with those of conventional estimators. This estimator is applied to a field experimental data set.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.275-280
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• 2003

공간 자료에서 이상점이 존재할 경우 변이도(Variogram)를 추정함에 있어 그 효과를 줄이기 위한 방법으로 로버스트(robust) 변이도를 이용한다. 그러나 이상점이 존재하는 자료분석에서 로버스트 변이도를 사용하기에 앞서 이상점을 수정한 자료를 사용하였을 경우 그 효율성 또한 좋다고 알려져 있다. 본 논문에서는 이상점이 존재하는 자료를 분석함에 있어 기존의 이상점 수정법 및 새로운 이상점 수정법의 효율성을 비교하였다.