Publication

Highly Robust Variogram Estimation

1996
Reports

Abstract

In the context of spatial statistics, the classical variogram estimator proposed by Matheron is not robust against outliers in the data, nor is Cressie and Hawkins' estimator. Therefore, we suggest the use of a variogram estimator based on a highly robust estimator of scale. The robustness properties of these three estimators are analysed and compared. Several simulations are carried out from a spherical underlying variogram, with various amounts of outliers in the data. Results show that the highly robust variogram estimator improves the estimation significantly

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1996
Reports

Abstract

Official source

Related concepts (21)

Estimator

In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. For example, the sample mean is a commonly used estimator of the population mean. There are point and interval estimators. The point estimators yield single-valued results. This is in contrast to an interval estimator, where the result would be a range of plausible values.

Robust regression

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations).

Robust statistics

Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from a parametric distribution.

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Highly Robust Variogram Estimation

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